[Federal Register Volume 69, Number 207 (Wednesday, October 27, 2004)]
[Notices]
[Pages 62748-62776]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 04-23771]



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Part II





Department of the Treasury





Office of the Comptroller of the Currency





Federal Reserve System

Federal Deposit Insurance Corporation

Department of the Treasury





Office of Thrift Supervision



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Internal Ratings-Based Systems for Retail Credit Risk for Regulatory 
Capital; Notice

  Federal Register / Vol. 69, No. 207 / Wednesday, October 27, 2004 / 
Notices  

[[Page 62748]]


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DEPARTMENT OF THE TREASURY

Office of the Comptroller of the Currency

[Docket No. 04-22]

FEDERAL RESERVE SYSTEM

[Docket No. OP-1215]

FEDERAL DEPOSIT INSURANCE CORPORATION

DEPARTMENT OF THE TREASURY

Office of Thrift Supervision

[No. 2004-48]


Internal Ratings-Based Systems for Retail Credit Risk for 
Regulatory Capital

AGENCIES: Office of the Comptroller of the Currency, Treasury (OCC); 
Board of Governors of the Federal Reserve System (Board); Federal 
Deposit Insurance Corporation (FDIC); and Office of Thrift Supervision, 
Treasury (OTS).

ACTION: Proposed supervisory guidance with request for comment.

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SUMMARY: The OCC, Board, FDIC, and OTS (Agencies) are publishing for 
industry comment a document that sets forth proposed supervisory 
guidance for banks, savings associations, and bank holding companies 
(banking organizations) that would use the internal-ratings-based (IRB) 
approach to determine their regulatory capital requirements for retail 
credit exposures. The Agencies described the IRB approach in general 
terms in an advance notice of proposed rulemaking (ANPR) in August 2003 
and expect to issue a notice of proposed rulemaking (NPR) in 2005 that 
would comprehensively implement the IRB approach and other elements of 
the International Convergence of Capital Measurement and Capital 
Standards: A Revised Framework, which was adopted by the Basel 
Committee on Banking Supervision in June 2004 (Basel II Framework). 
Under the IRB approach, banking organizations would use internal 
estimates of certain risk parameters as key inputs in the determination 
of their regulatory capital requirements. The Agencies intend for this 
guidance to provide banking organizations, in anticipation of the NPR, 
with a description of the current views of the Agencies regarding (and 
an opportunity for interested persons to comment on) the components and 
characteristics of a qualifying IRB credit risk measurement, data 
maintenance, segmentation, and quantification framework for retail 
exposures.

DATES: Comments must be submitted on or before January 25, 2005.

ADDRESSES: Comments should be directed to:
    OCC: Office of the Comptroller of the Currency, 250 E Street SW., 
Mail stop 1-5, Washington, DC 20219, Attention: Docket No. [04-22], Fax 
number (202) 874-4448 or Internet address: [email protected]. 
Comments may be inspected and photocopied at the OCC's Public 
Information Room, 250 E Street, SW., Washington, DC. You may submit 
comments, identified by docket number [04-22], by any of the following 
methods:
     Federal eRulemaking Portal: http://www.regulations.gov. 
Follow the instructions for submitting comments.
     OCC Web Site: http://www.occ.treas.gov. Click on ``Contact 
the OCC,'' scroll down and click on ``Comments on Proposed 
Regulations.''
     E-mail address: [email protected]. Please 
include docket number [04-22] in the subject line of the message.
     Fax: (202) 874-4448.
     Mail: Office of the Comptroller of the Currency, 250 E 
Street, SW., Public Reference Room, Mail Stop 1-5, Washington, DC 
20219.
     Hand Delivery/Courier: 250 E Street, SW., Attn: Public 
Reference Room, Mail Stop 1-5, Washington, DC 20219
    Board: You may submit comments, identified by Docket No. OP-1215, 
by any of the following methods:
     Agency Web Site: http://www.federalreserve.gov. Follow the 
instructions for submitting comments on the http://www.federalreserve.gov/generalinfo/foia/ProposedRegs.cfm.
     Federal eRulemaking Portal: http://www.regulations.gov. 
Follow the instructions for submitting comments.
     E-mail: [email protected]. Include docket 
number in the subject line of the message.
     Fax: (202) 452-3819 or (202) 452-3102.
     Mail: Jennifer J. Johnson, Secretary, Board of Governors 
of the Federal Reserve System, 20th Street and Constitution Avenue, 
NW., Washington, DC 20551.
    All public comments are available from the Board's Web site at 
http://www.federalreserve.gov/generalinfo/foia/ProposedRegs.cfm as 
submitted, except as necessary for technical reasons. Accordingly, your 
comments will not be edited to remove any identifying or contact 
information. Public comments may also be viewed electronically or in 
paper form in Room MP-500 of the Board's Martin Building (20th and C 
Streets, NW.) between 9 a.m. and 5 p.m. on weekdays.
    FDIC: You may submit comments by any of the following methods:
     Federal eRulemaking Portal: http://www.regulations.gov. 
Follow the instructions for submitting comments.
     Agency Web site: http://www.FDIC.gov/regulations/laws/federal/propose.html.
     Mail: Robert E. Feldman, Executive Secretary, Attention: 
Comments/Legal ESS, Federal Deposit Insurance Corporation, 550 17th 
Street, NW., Washington, DC 20429.
     Hand Delivered/Courier: The guard station at the rear of 
the 550 17th Street Building (located on F Street), on business days 
between 7 a.m. and 5 p.m.
     E-mail: [email protected].
     Public Inspection: Comments may be inspected and 
photocopied in the FDIC Public Information Center, Room 100, 801 17th 
Street, NW., Washington, DC, between 9 a.m. and 4:30 p.m. on business 
days.
    Instructions: Submissions received must include the agency name and 
title for this notice. Comments received will be posted without change 
to http://www.FDIC.gov/regulations/laws/federal/propose.html, 
including any personal information provided.
    OTS: You may submit comments, identified by No. 2004-48, by any of 
the following methods:
     Federal eRulemaking Portal: http://www.regulations.gov. 
Follow the instructions for submitting comments.
     E-mail: [email protected]. Please include No. 
2004-48 in the subject line of the message, and include your name and 
telephone number in the message.
     Fax: (202) 906-6518.
     Mail: Regulation Comments, Chief Counsel's Office, Office 
of Thrift Supervision, 1700 G Street, NW., Washington, DC 20552, 
Attention: No. 2004-48.
     Hand Delivery/Courier: Guard's Desk, East Lobby Entrance, 
1700 G Street, NW., from 9 a.m. to 4 p.m. on business days, Attention: 
Regulation Comments, Chief Counsel's Office, Attention: No. 2004-48.
    Instructions: All submissions received must include the agency name 
and docket number or Regulatory Information Number (RIN) for this 
rulemaking. All comments received will be posted without change to 
http://www.ots.treas.gov/pagehtml.cfm?catNumber=67&an=1,

[[Page 62749]]

including any personal information provided.
    Docket: For access to the docket to read background documents or 
comments received, go to http://www.ots.treas.gov/pagehtml.cfm?catNumber=67&an=1. In addition, you may inspect comments at the 
Public Reading Room, 1700 G Street, NW., by appointment. To make an 
appointment for access, call (202) 906-5922, send an e-mail to 
public.info@ots.treas.gov">public.info@ots.treas.gov, or send a facsimile transmission to (202) 
906-7755. (Prior notice identifying the materials you will be 
requesting will assist us in serving you.) We schedule appointments on 
business days between 10 a.m. and 4 p.m. In most cases, appointments 
will be available the next business day following the date we receive a 
request.

FOR FURTHER INFORMATION CONTACT:
    OCC: Mitchell Stengel, Senior Expert, Basel Credit Risk Modeling, 
Risk Analysis, (202) 874-5250; Daniel L. Pearson, National Bank 
Examiner, Credit Risk, (202) 874-5170; and Ron Shimabukuro, Special 
Counsel, Legislative and Regulatory Activities Division, (202) 874-
5190, Office of the Comptroller of the Currency, 250 E Street, SW., 
Washington, DC 20219.
    Board: Sabeth Siddique, Manager, (202) 452-3861, Division of 
Banking Supervision and Regulation; Mark E. Van Der Weide, Senior 
Counsel, (202) 452-2263, Legal Division, Board of Governors of the 
Federal Reserve System, 20th Street and Constitution Avenue, NW., 
Washington, DC 20551; and William W. Lang, Vice President, Supervision, 
Regulation and Credit, Federal Reserve Bank of Philadelphia, (215) 574-
7225. For users of Telecommunications Device for the Deaf (``TDD'') 
only, contact (202) 263-4869.
    FDIC: Peter Hirsch, Basel II Project Manager, (202) 898-6751, Jon 
Eagar, Senior Examiner, (801) 263-3090, ext. 4726, Division of 
Supervision and Consumer Protection; Michael B. Phillips, Counsel, 
(202) 898-3581, Legal Division, Federal Deposit Insurance Corporation, 
550 17th Street, NW., Washington, DC 20429.
    OTS: Fred Phillips-Patrick, Manager, Credit Risk, (202) 906-7295, 
Supervision Policy; Karen Osterloh, Special Counsel, (202) 906-6639, 
Chief Counsel's Office, Office of Thrift Supervision, 1700 G Street, 
NW., Washington, DC 20552.

SUPPLEMENTARY INFORMATION: The Agencies issued an ANPR on August 4, 
2003, which sought comment on a substantially revised capital adequacy 
framework for large and internationally active U.S. banking 
organizations. See 68 FR 45900. The content of the ANPR was based in 
large part on the April 2003 version of the Basel II Framework.\1\ 
Specifically, the ANPR described significant elements of the IRB 
approach for computing credit risk capital requirements and the 
Advanced Measurement Approaches for computing operational risk capital 
requirements (AMA approach). Under the ANPR, certain banking 
organizations would be required to adopt the IRB and AMA approaches 
(core banks) and other banking organizations that met certain criteria 
would have the ability to adopt the IRB and AMA approaches on a 
voluntary basis (opt-in banks). Under the IRB and AMA approaches 
outlined in the ANPR, core banks and opt-in banks would use internal 
estimates of certain risk components as key inputs in the determination 
of their regulatory capital requirements.
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    \1\ See The New Basel Capital Accord (April 2003) (available at 
http://www.bis.org). The Basel II Framework sets out both a 
Foundation and Advanced IRB approach. However, for purposes of 
domestic U.S. implementation, the ANPR only proposed adoption of the 
Advanced IRB approach.
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    Contemporaneously with the ANPR, the Agencies also issued for 
public comment two proposed supervisory guidance documents relating to 
the revised capital framework. See 68 FR 45949. The first document 
provided proposed supervisory guidance on IRB systems for corporate 
credit risk. This document described then-existing supervisory views on 
the credit risk measurement and management systems of banking 
organizations that intended to adopt the IRB approach for computing 
capital requirements for corporate credit risk exposures. The second 
document provided proposed supervisory guidance on AMA approaches for 
operational risk.
    In June 2004, the Basel Committee on Banking Supervision published 
a further revised version of the Basel II Framework.\2\ In light of the 
timetable for implementation of the Basel II Framework on an 
international basis and the complexity and long-term operational 
planning and program implementation needs of the core banks and opt-in 
banks, the Agencies are publishing for comment the following proposed 
IRB retail guidance document. The issuance of this document, together 
with the proposed IRB supervisory guidance on corporate credit risk and 
the proposed AMA supervisory guidance on operational risk, is part of 
an effort by the Agencies to gather as much industry feedback from 
interested parties as possible before the issuance of the NPR, which 
the Agencies expect will propose a revised capital adequacy standard 
based on the Basel II Framework for large and internationally active 
U.S. banking organizations. Issuing this proposed guidance before the 
formal issuance of the NPR will facilitate both (i) public input on the 
qualifying standards and infrastructure requirements for IRB and AMA 
and (ii) understanding of current Agency thinking for those banking 
organizations that expect to be core banks or opt-in banks and have 
sought additional guidance so that they may voluntarily begin 
operational planning to qualify for use of the IRB and AMA approaches 
at the earliest possible time.
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    \2\ See International Convergence of Capital Measurement and 
Capital Standards (June 2004) (available at http://www.bis.org). The 
Basel Committee on Banking Supervision is a committee of banking 
supervisory authorities that was established by the central bank 
governors of the Group of Ten countries in 1975. It consists of 
senior representatives of bank supervisory authorities and central 
banks from Belgium, Canada, France, Germany, Italy, Japan, 
Luxembourg, the Netherlands, Spain, Sweden, Switzerland, the United 
Kingdom, and the United States.
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    Banking organizations should note, however, that this retail IRB 
guidance, like the proposed corporate IRB guidance and the proposed AMA 
operational risk guidance, is only a proposal. Although these three 
proposed guidance documents reflect the views of the Agencies at the 
time of issuance concerning the elements of an appropriate IRB and AMA 
risk management infrastructure for core and opt-in banks, the guidance 
documents are subject to substantial change based on comments submitted 
by banking organizations and other interested parties, further analysis 
by the Agencies, results of a Quantitative Impact Study, evolution of 
the Basel II Framework, and technological advances in the risk 
measurement and management disciplines.
    The proposed retail guidance, like the proposed corporate IRB 
guidance and the proposed operational risk AMA guidance, includes many 
supervisory standards that ultimately may become part of the NPR rule 
text as proposed minimum qualifying requirements for use of the IRB and 
AMA approaches. The Agencies included these standards in the proposed 
guidance documents in order to provide banking organizations with 
coherent and comprehensive guidance as to the current views of the 
Agencies on the elements of an IRB and AMA risk management 
infrastructure. The proposed guidance documents do not reflect any 
final decisions by the Agencies about the content of the final rule, 
and no such decisions will be made by the Agencies prior to a full 
evaluation of the comments on the future NPR.

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Request for Comments

    The Agencies request comment on whether any of the standards set 
forth in this proposed retail IRB guidance should be revised, deleted, 
or supplemented, and which of these standards should be (1) mandatory 
minimum qualifying criteria for use of the retail IRB approaches, or 
(2) criteria for supervisory guidance purposes only.
    We seek comment on all other aspects of the following proposed 
retail guidance document as well, including (1) the important 
supervisory expectations (referred to as supervisory standards in the 
guidance document) that are designated in the document by the prefix 
``RS;'' (2) the methodology for the estimation of the three IRB 
segment-level credit risk parameters; and (3) the framework for the 
evaluation and oversight of retail exposure credit risk, which includes 
provisions covering segmentation, quantification, data maintenance, and 
control and oversight mechanisms.
    In particular, the Agencies are interested in industry comment on 
the following issues:
    1. Qualifying Revolving Exposures (QRE) Volatility Requirement. 
This proposed retail IRB guidance does not set forth criteria for 
defining what will constitute a ``low'' ratio of loss rate volatility 
to average loss rate for the purpose of qualification for QRE capital 
treatment. (See paragraphs 160 to 164 of the proposed guidance.) In 
developing the NPR, the Agencies will consider various options for 
addressing this concern and will provide additional information 
regarding QRE capital treatment. The Agencies seek comment on ways to 
implement the low volatility requirement for QRE sub-portfolios.
    2. Definition of Default. This proposed retail IRB guidance 
(paragraph 98) stipulates that a retail exposure will be considered in 
default if any one of three ``loss recognition events'' occurs. One of 
these three events is that ``The exposure is put on non-accrual 
status.''
    The Agencies acknowledge that there is not a requirement for 
placing delinquent retail exposures on nonaccrual status for either 
Call Report/Thrift Financial Report purposes or for GAAP. Nonetheless, 
many banks choose to put certain retail loans on nonaccrual and report 
these as such on their Call Reports/Thrift Financial Reports and 
financial statements.
    The Agencies invite comment on this particular element of the 
proposed definition of default, including detailed explanations of why 
banking organizations favor or oppose the inclusion of nonaccrual 
status in the definition of default.
    3. Loss Given Default (LGD) Estimation. When the loss severity of a 
retail portfolio exhibits significant cyclical variability, this 
proposed retail IRB guidance states that a bank must estimate an LGD 
that reflects periods of high credit losses for the particular 
portfolio (e.g., mortgages). The period of high credit losses may be 
different for each retail portfolio. (See standard RS-22 and paragraph 
127.) The Agencies invite comment on various issues related to 
estimating LGD for such periods:
     How should ``periods of high credit losses'' (also 
referred to as periods when credit losses are ``substantially higher 
than average'') for a portfolio be defined?
     What methods could be used to estimate an LGD appropriate 
to such periods?
     Should the LGD adjustment for high credit losses reflect 
the likely LGD when credit losses are high at the product or portfolio 
level for the particular bank (legal entity), or for a nationally 
diversified portfolio?
     How will a bank ensure that the LGD will reflect any 
unique or predictive risk characteristics of individual segments or 
small groups of segments if the period of high credit losses is defined 
at an aggregated level?
     If segments are defined across multiple legal entities, 
how will the banking organization ensure that the capital levels 
accurately reflect the unique risk of assets held by each legal entity?

The Agencies, through the Basel Committee on Banking Supervision, are 
undertaking additional work to clarify LGD estimation.
    4. Criteria for Assigning Exposures to Retail Categories. Because 
each risk category has its own risk-weight function, assignment to 
different risk categories results in different capital requirements. A 
variety of loan types, especially real estate loans, could be placed in 
more than one retail or corporate IRB risk category. The Agencies 
request comment on whether the criteria for assigning exposures to 
retail categories are appropriate for the credit risk of the exposures. 
For example, is four units the appropriate limit on the number of units 
in a residential property to meet the definition of a residential 
mortgage loan? In addition, are small business loans appropriately 
categorized based on whether they are primarily or partially secured by 
residential real estate?

Paperwork Reduction Act

    Each of the Agencies is subject to the Paperwork Reduction Act of 
1995 (PRA).\3\ The rulemaking initiated by the ANPR likely will impose 
requirements for core and opt-in banks, either in the regulations 
themselves or as part of interagency implementation guidance, that are 
covered by the PRA. This proposed retail IRB guidance describes the 
current views of the Agencies as to the components and characteristics 
of a qualifying IRB credit risk measurement, data maintenance, 
segmentation, and quantification framework for retail exposures. It is 
important that banking organizations recognize in reviewing the 
proposed guidance that it is subject to substantial change based on the 
comments received during the rulemaking process, further analysis by 
the Agencies, evolution of the Basel II Framework, and other 
developments.
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    \3\ 44 U.S.C. 3501 et seq.
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    Commenters on this proposed retail IRB guidance are asked to 
provide any estimates that they can reasonably determine about the 
time, effort, and financial resources that will be required to develop 
and maintain the plans, reports, and records discussed in the proposed 
guidance. Commenters also are requested to specify whether the 
described capital and methodological standards would necessitate the 
acquisition or development or new compliance/information systems or the 
significant modification of existing compliance/information systems.
    The Agencies also invite comment on:
    (1) Whether the collections of information contained in the 
proposed guidance are necessary for the proper performance of each 
agency's functions, including whether the information has practical 
utility;
    (2) What would be an accurate estimate of the burden of the 
proposed information collections;
    (3) Ways to enhance the quality, utility, and clarity of the 
information to be collected;
    (4) Ways to minimize the burden of the information collections on 
respondents, including the use of automated collection techniques or 
other forms of information technology; and
    (5) Estimates of capital or start-up costs and costs of operation, 
maintenance, and purchases of services to provide information.
    Respondents/recordkeepers are not required to respond to any 
collection of information unless it displays a currently valid Office 
of Management and Budget (OMB) control number.

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    The Agencies have issued the proposed retail IRB guidance to seek 
public input on the content of the guidance and information collection 
methods used in the guidance. The Agencies have made no determination 
regarding the information to be collected, if any. When the Agencies 
have developed a firm proposal, they will follow the standard process 
to seek public comment on the information collection and to obtain OMB 
approval.
    The Agencies will use any comments received to evaluate the burden 
attendant to the approach set forth in the proposed retail IRB 
guidance. Comments on the collections of information should be sent to:
    OCC: John Ference or Camille Dixon, OCC Clearance Officer, Office 
of the Comptroller of the Currency, 250 E Street, SW., Mail Stop 8-4, 
Attention: 1557-IRBG, Washington, DC 20219. Comments also may be sent 
by electronic mail to [email protected].
    Board: Cindy Ayouch, Federal Reserve Board Clearance Officer, (202) 
452-3829, Division of Research and Statistics, Board of Governors of 
the Federal Reserve System, 20th and C Streets, NW., Mail Stop 41, 
Washington, DC 20551. Comments also may be sent by electronic mail to 
[email protected].
    FDIC: Leneta Gregorie, Counsel, (202) 898-3907, Legal Division, 
Federal Deposit Insurance Corporation, 550 17th Street, NW., 
Washington, DC 20429. Comments also may be sent by electronic mail to 
[email protected].
    OTS: Marilyn K. Burton, OTS Clearance Officer, (202) 906-6467, 
Office of Thrift Supervision, 1700 G Street, NW., Washington, DC 20552. 
Comments also may be sent by electronic mail to 
[email protected].
    The text of the proposed IRB retail guidance document follows:

Proposed Supervisory Guidance on Internal Ratings-Based Systems for 
Retail Credit Risk

Table of Contents

I. Introduction

A. Background
B. Scope of Retail Guidance
C. Definition of Retail Exposures
D. Quantifying Retail Exposure Credit Risk
E. Supervisory Expectations

II. Retail Risk Segmentation Systems for IRB

A. Overview
B. Criteria for Retail Segmentation
C. Retail Risk Segmentation Architecture
    1. Migration of Exposures Between Retail Segments
    2. Frequency of Changes to the Segmentation System
    3. Segmentation and the Recognition of the Risk Mitigation 
Benefits of Guarantees and Insurance
D. Validation Process
    1. Segmentation Systems' Developmental Evidence
    2. Ongoing Monitoring
    3. Back-testing of the Segmentation System

III. Quantification of IRB Systems

A. Introduction
    1. The Four Stages of the Quantification Process
    2. Integration of the Four Stages
    3. General Standards for Sound IRB Quantification
B. Quantification of the IRB Risk Parameters
    1. Quantification of Probability of Default (PD)
    a. Data
    b. Estimation
    1. Seasoning
    c. Mapping
    d. Application
    2. Quantification of Loss Given Default (LGD)
    a. Data
    b. Estimation
    c. Mapping
    d. Application
    3. Quantification of Exposure at Default (EAD)
    a. Introduction
    b. Data
    c. Estimation d.Mapping e.Application
C. Quantification: Special Cases and Applications
    1. Small Business Exposures
    2. Retail Leases
    3. Purchased Retail Receivables
    4. Loan Sales
    5. Securitization and Undrawn Balances
    6. Multiple Legal Entities
    7. QRE Treatment Qualification
    8. Stress Testing
D. Validation
    1. Introduction
    2. Developmental Evidence
    3. Ongoing Process Verification and Benchmarking
    4. Back-Testing

IV. Data Maintenance

A. Overview
B. General Data Requirements
    1. Standards for Refreshed Data
    2. Loan Sales
    3. Validation and Refinement
    4. Data Standards for Outsourced Activities
    5. Calculating Capital Ratios and Reporting to the Public
C. Managing Data Quality and Integrity
    1. Documentation and Definitions
    2. Data Access and Scalability
    3. Data Gaps

V. Control and Oversight Mechanisms

A. Overview
B. Controls Over Lending Activities
C. Accountability
D. Independent Review of Retail IRB Processes
E. Transparency
F. Use of Risk Estimates
G. Internal and External Audit
H. Corporate Oversight

Appendix A: Process Analysis Examples

Appendix B: Technical Examples

List of Acronyms

I. Introduction

A. Background

    1. This document provides supervisory guidance for banks, thrifts, 
and bank holding companies that adopt the advanced internal-ratings-
based (``IRB'') approach for determining regulatory risk-based capital 
requirements for retail exposures (``banks'').\4\ As described in the 
preamble to the Federal Register publication of this guidance, this 
document reflects the current views of the Federal banking agencies 
(``agencies'') and is subject to change based on comments submitted by 
the banking industry and other interested parties, further analysis by 
the agencies, results of the fourth quantitative impact study, and 
technological advances in the risk measurement and management 
disciplines. This retail guidance includes some supervisory standards 
that ultimately may become part of the minimum IRB qualifying 
requirements that would be proposed as part of the notice of proposed 
rulemaking (``NPR'') that the agencies intend to issue for public 
comment in 2005 to comprehensively implement the IRB approach. It was 
necessary to include these standards in this proposed guidance document 
in order to provide banks with coherent and comprehensive guidance as 
to the current views of the agencies on the elements of a retail IRB 
risk management infrastructure.
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    \4\ Throughout this guidance, the term ``banks'' generally 
refers to banks, thrifts, and bank holding companies adopting the 
IRB approach.
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    2. A central objective of the IRB framework is to enhance the risk 
sensitivity of the minimum regulatory capital requirements. Under the 
retail IRB approach, banks assign risk parameters to pools of exposures 
with similar risk characteristics, that is, to risk segments, rather 
than to individual exposures (as in the corporate portfolio). These 
parameters are then used for the determination of minimum regulatory 
capital. Supervisors will rely on banks, subject to minimum standards, 
to use internal risk management systems to differentiate segments of 
retail exposures by the credit risk they pose and to quantify the risk 
parameters for each segment. Adequate data to support accurate and 
reliable credit risk measurements, as well as rigorous management 
oversight and controls, including continual monitoring and

[[Page 62752]]

validation, are crucial to the prudent application of the IRB capital 
framework.
    3. This guidance, which is written for supervisors and banks, 
describes the components and characteristics of an IRB credit risk 
measurement and management framework for retail exposures. The guidance 
explains how to measure the risk of retail exposures, maintain data on 
them, segment them, and quantify each segment's risk. The guidance 
should help foster accountability, transparency, and oversight and 
control mechanisms in the IRB capital framework.
    4. With these goals in mind, this guidance sets forth retail 
supervisory standards for an IRB credit risk system. These standards 
are highlighted in bold and designated by the prefix ``RS.'' To enable 
banks to implement the framework flexibly whenever possible, these 
regulatory standards typically take the form of general principles 
rather than specific requirements. However, when the need for 
uniformity outweighs the benefits of flexibility (often for reasons of 
prudence), the guidance provides more detailed and specific 
expectations. Banks would be expected to have credit risk management 
practices that are consistent with the substance and spirit of the 
standards in this guidance. Furthermore, nothing in this guidance 
should be interpreted as weakening, modifying, or superseding the 
safety and soundness principles articulated in the existing statutes, 
regulations, or guidance of the agencies.
    5. In general, this IRB retail guidance neither dictates the 
precise manner by which banks should seek to meet the supervisory 
standards nor provides comprehensive technical guidance on how to meet 
the standards. This document assumes that readers are familiar with the 
proposed IRB approach for the calculation of minimum regulatory capital 
requirements in the International Convergence of Capital Measurement 
and Capital Standards, published by the Basel Committee on Banking 
Supervision in June 2004 (``Basel II'').
    6. Under the retail IRB approach, banks first segment retail 
exposures and then quantify the risk of each segment by estimating each 
segment's probability of default (PD), loss given default (LGD), and 
exposure at default (EAD). Consistent with many retail lenders' 
internal risk management practices, a bank may also choose to 
indirectly obtain an estimate of PD by first obtaining estimates of 
average dollar loss rates and loss severity. These quantitative 
estimates of risk must be consistent with those used for internal risk 
management purposes.

B. Scope of Retail Guidance

    7. For the purposes of this guidance, the terms ``retail exposure'' 
and ``retail loan'' are intended to include retail leases as well as 
loans.
    8. When the terms ``models'' and ``models-based'' are used in this 
guidance, they refer to banks'' use of various types of statistical 
modeling techniques solely for the purpose of estimating the risk 
parameters PD, LGD, and EAD for IRB retail segments.
    9. The agencies expect that this guidance and the standards set 
forth below would apply to most retail exposures of banks. Although 
banks can designate some retail exposures as nonmaterial and, thus, not 
subject to the retail IRB approach, the aggregate amount of these 
nonmaterial retail exposures must be small as a percentage of the 
bank's total retail exposures, and the aggregate amount of credit risk 
in the nonmaterial retail portfolios must be a small percentage of the 
bank's total amount of retail exposure credit risk. A bank must 
maintain adequate documentation to support its nonmaterial 
determinations. Subject to supervisory review, banks will determine 
minimum capital requirements for a nonmaterial retail portfolio 
according to the risk-based capital standards for non-IRB banks.
    10. Some banking organizations have retail portfolios that are 
centrally managed, even though the exposures are held by multiple legal 
entities. Certain activities, including segmentation and 
quantification, can be conducted across multiple legal entities within 
the United States, subject to limitations discussed in chapter III and 
chapter V. However, each legal entity subject to IRB capital 
requirements must document its minimum regulatory capital requirements 
on a standalone basis and hold its own separate minimum regulatory 
capital in proportion to the risk exposure of its portfolios. 
Specifically, the PD, LGD, and EAD estimates used to determine minimum 
regulatory capital levels must be applied to exposures at the segment 
level, and capital requirements for each relevant legal entity should 
be based on the proportionate share of each segment owned by such legal 
entity. Furthermore, the board of directors of each such legal entity 
must ensure that capital calculations accurately reflect the risk 
profile of their individual banks.
    11. While the general principles of retail segmentation, 
quantification, and data maintenance will apply to all portfolios, 
special issues may arise in the case of portfolios outside the United 
States. Cross-border issues for retail and other portfolios will be 
addressed in future documents.

C. Definition of Retail Exposures

    12. An exposure is a retail exposure for IRB purposes if both of 
the following conditions are met:
     The exposure is managed as part of a pool of similar 
exposures rather than as an individual exposure; and
     With the exception of small business loans (see below), 
the obligor is an individual.
    13. Within this general definition, there are three retail risk 
categories, each with specific qualifying criteria:
     Residential mortgage loans secured by one- to four-family 
residential properties. Includes first and subsequent liens, term 
loans, lines of credit, and legally binding commitments to lend. This 
includes business loans if the loans are primarily secured by one- to 
four-family residential properties. No limit on the size of the 
exposure.
     Qualifying revolving exposures (QREs) whose outstanding 
amount fluctuates, determined largely by the borrower's decisions to 
borrow and repay, up to a pre-established limit. Must be revolving, 
unsecured, and unconditionally cancelable by the bank; maximum 
exposure, $100,000. Includes most credit cards to individuals (but not 
those issued on behalf of a business) and overdraft lines on individual 
checking accounts. Also included are overdraft protection programs, 
commonly referred to bounced-check protection programs, that advise 
customers of an amount up to which overdrafts may be paid.\5\ To 
qualify for QRE status, a sub-portfolio must display low volatility of 
loss rates relative to its average level of loss rates.
---------------------------------------------------------------------------

    \5\ This sentence is intended to capture bounced-check 
protection programs and reflects the reporting and capital standards 
proposed in the draft Interagency Guidance on Overdraft Protection 
Programs that was published for comment in the Federal Register on 
June 7, 2004 (69 FR 31858). However, it should be noted that once 
the Interagency Guidance on Overdraft Protection Programs is 
finalized, this draft guidance may be amended to reflect changes in 
that guidance.
---------------------------------------------------------------------------

     Other retail--general and small business. ``General'' 
applies to all retail exposures to individuals that do not fall into 
either of the two previous categories or into the ``small business'' 
category described immediately below. No limit on size of exposure. 
``Small business'' applies to small loans of any kind to individuals or 
companies for business purposes. However, if a small business loan is 
primarily secured by 1-4 family residential property, it should

[[Page 62753]]

be included in the residential mortgage category above. For small 
business loans, total exposure to a single borrower is limited to $1 
million, on a fully consolidated basis, although supervisors may allow 
amounts slightly above the limit.
    14. Private banking exposures must meet the requirements stated 
above, including the requirement that they must be managed as part of a 
pool of similar exposures, to be considered under retail IRB. 
Otherwise, they would fall under corporate IRB.
    15. Each of the three retail risk categories has a separate risk-
weight function. These functions differ from one another only by the 
supervisor-specified asset value correlation. The unexpected loss 
capital requirement (K) per dollar of EAD for each retail segment of 
non-defaulted assets is calculated using the following general formula:
[GRAPHIC] [TIFF OMITTED] TN27OC04.000

where N is the cumulative standard normal distribution, 
N-\1\ is the inverse cumulative standard normal 
distribution, R is the asset value correlation, and 0.999 is the 
``solvency standard'' chosen by the supervisors.\6\ For residential 
mortgages, R is specified as 0.15, for qualifying revolving exposures, 
R is specified as 0.04, and for other retail exposures, R varies 
between 0.03 and 0.16, based on the following formula:
---------------------------------------------------------------------------

    \6\ That is, minimum regulatory capital for covering unexpected 
losses, K, is set to equal the estimated level of unexpected losses 
corresponding to the 99.9th percentile of the loss distribution for 
the bank's credit portfolios.
[GRAPHIC] [TIFF OMITTED] TN27OC04.001

    16. Minimum capital requirements for defaulted retail exposures are 
determined separately. See chapter III for a detailed discussion.
    17. Risk-weighted assets (RWA) for each segment are calculated as 
12.5 x K x EAD.
    18. The expected dollar loss on a segment (EL) is defined as PD x 
LGD x EAD. The overall level of expected losses in the retail and 
certain other portfolios is used in the calculation of a regulatory 
capital adjustment.

D. Quantifying Retail Exposure Credit Risk

    19. There are two distinct phases in the process of determining the 
minimum regulatory capital requirements for the credit risk of retail 
exposures. In the first phase, credit risk segmentation, a bank assigns 
every individual retail exposure to a segment or pool with homogeneous 
risk characteristics. These characteristics, often referred to as 
``primary risk drivers'' (such as loan-to-value ratios and credit 
scores), are reliable predictors of loan performance over time that 
allow banks to effectively sort exposures into homogeneous segments. To 
segment risk in this way, bankers must have a thorough understanding of 
how a retail exposure's risk drivers affect the risk parameters (PD, 
LGD, and EAD).
    20. In the second phase, quantification, a bank statistically 
estimates the three risk parameters, PD, LGD, and EAD, for each retail 
segment. Historical data are used to create ``reference segments'' 
whose subsequent credit performance has been observed and included in 
the data set. The central assumption of this phase is that the 
estimated relationship between the particular set of risk drivers and 
the credit performance of the reference segments will hold for the 
segments that make up the existing portfolio. Once the risk parameters 
are quantified for existing retail exposure segments, the bank then 
calculates the minimum regulatory capital requirements based on the 
appropriate IRB formulas.
    21. Each phase has its own validation challenges. In phase one, the 
bank must determine whether the assignment of retail exposures to 
segments effectively separates exposures by characteristics that remain 
significant drivers of risk over time. In phase two, the bank must 
determine whether the risk parameter estimates are accurate and 
representative of the risk in the existing portfolio.
    22. A robust and detailed data maintenance system should support 
implementation of the IRB segmentation and quantification process as 
well as their dynamic development. Management oversight and control 
mechanisms over the entire IRB retail credit risk system (including 
segmentation, quantification, and supporting data maintenance) should 
ensure conservative, verifiable, and accurate estimates of the segment-
level credit risk parameters.
    23. In summary, IRB banks will be expected to construct and 
maintain a retail credit system comprising four interdependent 
components corresponding to the four chapters of this guidance. The 
four chapters are organized as follows: chapter II, ``Segmentation''; 
chapter III, ``Quantification''; chapter IV, ``Data Maintenance''; and 
chapter V, ``Control and Oversight Mechanisms.''

E. Supervisory Expectations

    24. Taken together, segmentation, quantification, data maintenance, 
and control and oversight mechanisms provide a framework for defining 
and improving evaluation of retail credit risk and determining minimum 
regulatory capital. Supervisors expect that banks will continue to 
refine their credit risk systems using regular reviews and updates.
    25. All aspects of the risk segmentation system and the 
quantification processes must be subject to thorough, independent, and 
well-documented validation. Banks should use a variety of validation 
approaches; no single approach can conclusively validate the risk 
segmentation and quantification methods. Three broad types of useful 
tools include evaluating the developmental evidence or logic of the 
system; ongoing monitoring of system implementation and reasonableness 
(verification and benchmarking); and comparing realized outcomes with 
predictions (back-testing).
    26. A rigorous framework of control and oversight mechanisms must 
govern the entire IRB implementation. The framework must be 
characterized by independence, transparency, and accountability; must 
ensure that the IRB implementation standards discussed in this guidance 
are met; and must ensure that related bank policies are followed. The 
control and oversight mechanisms must also include independent 
technical validation of all quantitative aspects of the risk 
segmentation and quantification systems.
    27. For IRB systems to work successfully, they need the active

[[Page 62754]]

support and oversight of the board of directors and senior management 
to ensure that the various components fit together seamlessly and that 
incentives are in place to extend the system rigorously across business 
line, risk management, and other control groups.
    28. The proposed regulatory minimum capital requirements are 
predicated on a bank's internal systems being sufficiently advanced to 
allow a full and accurate assessment of its risk exposure. The IRB 
framework demands more rigorous validation work and controls than 
supervisors have required in the past. When properly implemented, the 
new framework will better align minimum capital requirements with risk.
    29. Supervisors will evaluate compliance with the four components 
of a retail IRB system and how well the various components of a bank's 
retail IRB system complement and reinforce one another to achieve the 
overall objective of accurately determining minimum required regulatory 
capital for retail exposures. In performing their evaluation, 
supervisors will exercise considerable supervisory judgment in 
evaluating both the individual components and the overall IRB 
framework.

II. Retail Risk Segmentation Systems for IRB

A. Overview

    30. This chapter describes the design and operation of a qualifying 
retail risk segmentation system. IRB retail risk segments are pools of 
exposures within the three retail risk categories that contain 
exposures with similar risk characteristics.
    31. The retail IRB framework is intended to provide banks with 
substantial flexibility to use the retail portfolio segmentation they 
believe is most appropriate for their activities, subject to the 
following broad standards:
     The goal of segmentation is to provide meaningful 
differentiation of risk, with each pool composed of exposures with 
homogeneous risk characteristics Accordingly, in developing the risk 
segmentation system, banks should consider the chosen risk drivers' 
ability to separate risk consistently over time and the overall 
robustness of the bank's approach to segmentation.
     Segmentation must use relevant borrower risk 
characteristics (such as credit score, delinquency, or debt-to-income 
ratio) and loan-related risk characteristics (such as loan-to-value or 
product type) that reliably differentiate a segment's risk from that of 
other segments and that perform consistently over time.
     Risk drivers for segmentation should be consistent with 
the predominant risk characteristics used by the bank for internal 
credit risk measurement and management.
     The segmentation system should generate pools that 
separate exposures by realized performance. It should be designed so 
that actual long-run outcomes closely approximate the retail IRB risk 
parameters estimated by the bank.
     In general, segments should not cross national 
jurisdictions.
     IRB banks must have ongoing validation processes for risk 
segmentation systems that include the evaluation of developmental 
evidence or logic of the system, ongoing monitoring, and back-testing. 
Validation for the risk segmentation system is ultimately tied to 
validation of the bank's quantification of IRB risk parameters. This 
aspect of validation is discussed in chapter III.
    32. The IRB retail risk parameter estimates that determine minimum 
required capital are assigned at the segment level.

B. Criteria for Retail Segmentation

    RS-1: Banks must segment exposures into pools with homogeneous risk 
characteristics. Banks must separately segment exposures in each 
distinct product line within each of the three retail risk categories 
(mortgage, QRE, and other).
    33. Examples of acceptable approaches to segmentation include:
     Banks may segment exposures by common risk drivers that 
are deemed relevant and material in determining the loss 
characteristics of a particular retail product. For example, a bank may 
segment mortgage loans by LTV band, age from origination, geography, 
origination channel, and credit score. Statistical modeling, expert 
judgment, or some combination of the two may determine the most 
relevant risk drivers.
     Alternatively, banks could segment by grouping loans with 
similar loss characteristics, such as similar average loss rates or 
similar PDs. (Those loss parameters would be estimated in accordance 
with the techniques outlined in chapter III.)
    34. While banks have considerable flexibility in determining IRB 
retail risk segments, they should consider factors affecting both 
borrower risk characteristics (such as credit score) and loan-related 
risk characteristics (such as LTV) when determining segmentation 
criteria.
    35. Each retail risk segment will typically be associated with a 
separate PD, LGD, and EAD. In some cases, it may be reasonable to use 
the same LGD estimate for multiple segments. In such cases, the bank 
must demonstrate that there are no material differences in LGD among 
those segments. Over time, supervisors expect banks to develop more 
precise data and methodologies for determining LGDs.
    36. There may be situations in which data for certain retail loans 
are missing or incomplete, such as data for purchased loans or loans 
originated as policy exceptions. The overall segmentation system should 
adequately consider the risk associated with these loans based on data 
availability. In some cases, missing or incomplete data by itself may 
be a significant risk factor for segmentation purposes.
    RS-2: Defaulted assets must be segmented on the basis of risk 
characteristics predictive of loss and recovery rates.
    37. The IRB capital calculation for defaulted assets requires banks 
to provide a ``best estimate'' of the losses on these loans. (See 
chapter III for details.) Since, by definition, defaulted assets have 
PDs equal to 1, these best estimates of losses will depend solely on 
banks' estimates of losses given current conditions. To produce these 
best estimates, banks must segment defaulted assets separately from 
non-defaulted assets, and base the segmentation on those 
characteristics that are most predictive of current loss and recovery 
rates. This segmentation should provide meaningful differentiation so 
that individual loans within each defaulted segment do not have 
material differences in their expected loss severity.
    RS-3: A retail IRB risk segmentation system must produce segments 
within each retail risk category that adequately differentiate risk and 
produce reliable estimates of the IRB risk parameters.
    38. A bank must support the degree of granularity in its 
segmentation system and the distribution of exposures across segments. 
Granularity refers to how finely the portfolio is segmented into 
differentiated risk pools.
    39. Banks have considerable flexibility in determining the 
granularity of their risk segmentation. Each bank must perform its own 
internal analysis to determine the appropriate degree of granularity in 
order to achieve the goal of producing homogeneous risk segments. For 
example, a bank using credit score ranges to segment its portfolio must 
provide the rationale for the ranges chosen.

[[Page 62755]]

    40. A concentration of exposures in a segment (or segments) does 
not, by itself, reflect a deficiency in the segmentation system. For 
example, a bank may lend within a narrow risk band and, therefore, have 
a smaller number of risk segments than a bank that lends across a wider 
range of risk bands. However, a bank with a high concentration of 
exposures in a particular risk segment will be expected to document 
that the bank's segmentation criteria are carefully delineated and well 
documented. The bank should be able to demonstrate that there is little 
risk differentiation among the exposures within the segment, and that 
the segmentation method produces reliable estimates of IRB risk 
parameters.
    RS-4: Banks must clearly define and document the criteria for 
assigning an exposure to a particular retail risk segment. The risk 
factors used for IRB risk segmentation purposes must be consistent with 
internal methods of assessing credit risk for retail exposures.
    41. The method of risk segmentation will help determine the risk 
parameters as well as which techniques should be used for validation 
and which control mechanisms will best ensure the integrity of the risk 
segmentation system. To assist the discussion of segmentation 
requirements, described below are some alternative techniques for 
determining appropriate segmentation.
     Banks may incorporate results of statistical underwriting 
models or scoring models directly into their risk segmentation process. 
For example, a bank may use a custom or bureau credit score as a 
segmenting criterion. In that case, the bank must validate the choice 
of the score, as well as demonstrate that its credit scoring system has 
adequate controls.
     Banks may incorporate the variables from a statistical 
model into their risk segmentation processes. For example, a bank that 
uses a statistical model to predict losses for its mortgage portfolio 
could select some or all of the major inputs to that model, such as 
debt-to-income and LTV, as segmentation criteria. As part of its 
validation and controls for the IRB segmentation system, the bank must 
provide an appropriate rationale and empirical evidence for its choice 
of the particular set of risk drivers from the loss prediction model.
     Banks may combine expert judgment with statistical 
analysis in determining appropriate segmentation criteria. However, 
expert judgment of this type must be well documented and supported by 
empirical evidence demonstrating that the chosen risk factors are 
reliable predictors of risk.
    42. A bank must be able to demonstrate a strong relationship 
between IRB risk drivers and comparable measures used for credit risk 
management. Specifically, a bank should demonstrate that the IRB 
segmentation system differentiates credit risk across the portfolio and 
captures changes in the level and direction of credit risk that are 
similar to measures used in credit risk management. For example, even 
if a bank uses custom scores for underwriting or account management, 
generic bureau scores may be used for IRB segmentation purposes if the 
bank can demonstrate a strong correlation between these measures.

C. Retail Risk Segmentation Architecture

Migration of Exposures Between Retail Segments
    RS-5: Banks must develop and document their policies to ensure that 
risk driver information is sufficiently accurate and timely to track 
changes in underlying credit quality and to migrate exposures between 
segments.
    43. Under the IRB framework, a bank initially assigns retail 
exposures to segments based on the information about their risk drivers 
available at the time of origination or acquisition. The bank must then 
continue to monitor the risk characteristics of the exposures and 
migrate exposures to new segments, as necessary, based on refreshed 
information gathered by the bank as part of its monitoring process.
    44. Banks must choose risk drivers that accurately reflect the risk 
of an exposure. Risk drivers selected should be consistent with risk 
measures used for credit risk management.
    45. In accordance with industry practices in retail credit risk 
management, a bank must have a well-documented policy on monitoring and 
updating information on exposure risk characteristics and on migrating 
exposures between segments. The policy should specify the risk 
characteristics to be updated and the frequency of updates for each 
product type or sub-portfolio within its retail portfolio. Updating of 
relevant information on these risk drivers must be consistent with 
sound risk management.
    46. Decisions regarding frequency of obtaining refreshed 
information should reflect the specific risk characteristics of 
individual segments and/or the materiality of the potential impact on 
capital. The frequency of updates and of migration will generally 
differ for different risk drivers and for different products. The 
underlying principle is that, in every period, retail exposures are 
assigned to segments that accurately reflect their risk profile and 
produce accurate IRB risk parameters.
    47. Banks are expected to assess their approach to updating 
information and migrating exposures as part of the validation of the 
segmentation process.
Frequency of Changes to the Segmentation System
    RS-6: Banks must review their segmentation system at least annually 
and have clear policies to define the criteria for modifying the 
system.
    48. Banks must review their segmentation system to ensure that it 
maintains adequate risk separation. Changes in the segmentation system 
should be documented and supported to ensure consistency and obtain 
historically comparable measurements.
Segmentation and the Recognition of the Risk Mitigation Benefits of 
Guarantees and Insurance
    RS-7: Banks that design their risk segmentation systems to realize 
the benefits of guarantees or other risk mitigants must be able to 
support their approach.
    49. Retail exposures may have guarantees or insurance, such as 
private mortgage insurance (PMI) and government guarantees for 
residential mortgages. (See chapter III for a more detailed discussion 
of PMI.) A bank's risk segmentation system may reflect such guarantees, 
as may its risk parameter estimates. For example, loans with similar 
risk characteristics, including the same type of guarantee, could be 
pooled together.

D. Validation Process

    RS-8: Banks must validate that their retail IRB risk segmentation 
process separates exposures into segments with homogeneous risk 
characteristics that generate reliable long-run estimates of the IRB 
risk parameters.
    50. Banks must ensure that the actual performance of their segments 
is consistent with the expectations underlying the assignment of 
exposures to segments as set forth in their documentation. Over time, 
performance data should validate the manner in which the bank 
differentiated the portfolio by segment, and the actual loss 
characteristics of each segment should be consistent with its estimated 
IRB risk parameters.
    RS-9: The ongoing validation process must include the review of

[[Page 62756]]

developmental evidence, ongoing monitoring, and back-testing.
    51. The ongoing process to confirm and ensure the performance of 
the segmentation system consists of:
     The evaluation of developmental evidence;
     Ongoing monitoring of system implementation and 
reasonableness; and
     Back-testing (comparing actual to predicted outcomes).
    52. IRB banks are expected to employ all of the components of this 
process. However, back-testing of segmentation may be difficult if a 
bank's process for modeling risks is evolving significantly. Therefore, 
banks may at times need to rely more heavily on developmental evidence 
and quality control tests to assure themselves and other interested 
parties that their segmentation systems are sufficiently accurate.
Segmentation Systems' Developmental Evidence
    53. Developmental evidence helps determine whether the segmentation 
system can be expected to differentiate effectively between pools of 
exposures by the credit risk they pose. To evaluate developmental 
evidence, experts make a reasonable assessment of the quality of the 
segmentation system by analyzing its design and construction.
     For example, developmental evidence in support of 
statistical techniques used in the segmentation process, such as 
scoring models or underwriting models, must include documentation and 
discussion of their logical foundations and an analysis of the 
statistical model-building techniques.
     The developmental evidence will be more persuasive when it 
includes empirical evidence of how well the segmentation system has 
differentiated pools of exposures in the past, including evidence that 
it worked effectively outside the development sample.
     Empirical developmental evidence of a segmentation system 
would also include evidence of how the system compares with other 
systems. These other systems could include other internal segmentation 
systems as well as external systems whose performance can be charted 
against industry benchmarks.
Ongoing Monitoring
    54. The second source of analytical support for the validity of a 
bank's risk segmentation system is the ongoing analysis to confirm that 
the system continues to group loans into pools with similar loss 
characteristics. The bank must develop a monitoring process to evaluate 
its system's ability to segment by risk and to apply this process 
consistently over time. The bank must document its approach to 
monitoring and the results of this analysis. The bank must also 
generate reports to senior management on the functioning of the 
segmentation system.
    55. Specific verification activities will depend on the 
segmentation approach. For retail lending, statistical models will be 
an important part of the segmentation process, and the bank must verify 
that the data used by these models are accurate and complete.
Back-Testing of the Segmentation System
    RS-10: Banks must establish internal tolerance limits for 
differences between expected and realized outcomes that require 
appropriate managerial review.
    56. Back-testing is comparing realized outcomes with each segment's 
expected performance. For retail IRB systems, back-testing is a means 
of assessing whether the bank's method of segmentation and its 
techniques for estimating IRB risk parameters combined to work 
effectively. Accordingly, back-testing is a conceptual bridge between 
the segmentation system discussed in this chapter and the 
quantification of the IRB risk parameters discussed in chapter III. 
Because these two processes are so closely linked, a more complete 
discussion of back-testing is deferred until chapter III.

III. Quantification of IRB Systems

A. Introduction

    57. The IRB framework requires banks to assign to each segment of 
the retail portfolio specific numerical values for each of the three 
risk parameters: probability of default (PD), loss given default (LGD), 
and exposure at default (EAD).\7\ Under the IRB framework, these 
numerical values are inserted into the appropriate formula (set forth 
in the introduction) to determine the minimum required regulatory 
capital for each segment.
---------------------------------------------------------------------------

    \7\ A note on units of measurement: PD and LGD are measured as 
rates (percentages or decimals). EAD is a dollar amount, 
representing estimated exposure at default. Therefore PD x LGD x EAD 
will represent the dollar amount of expected losses (EL).
---------------------------------------------------------------------------

    58. Quantification is the process by which these numerical values 
for each retail segment are determined. The risk parameters must be 
estimated in a manner consistent with sound risk management practice, 
quantitative techniques, and supervisory standards. In addition, a bank 
must ensure that these estimates remain valid over time. Since 
quantification occurs at the segment level, it is founded on the retail 
risk segmentation system presented in chapter II.
    59. Conceptually, the quantification process can be broken into 
four stages: obtaining historical reference data; using the historical 
reference data to estimate relationships between the risk 
characteristics of the borrowers and loans on the one hand and observed 
outcomes (such as default rate, loss severity rate, or tendency to make 
additional draws on credit card lines prior to default) on the other; 
mapping the correspondence between the reference data and the existing 
portfolio's data; and applying the relationship between reference data 
and parameters to the portfolio's data in order to generate IRB risk 
parameters for the bank's existing retail segments.
    60. In addition, the estimated values of the risk parameters (PD, 
LGD, and EAD) must be independently and thoroughly validated and the 
results reported to senior management.
    61. The chapter is organized as follows: Section A, 
``Introduction,'' establishes the organizing framework for IRB 
quantification and develops general standards that apply to the entire 
process. Section B, ``Quantification of the IRB Risk Parameters,'' 
covers specific supervisory standards that apply to the quantification 
of the three risk parameters, PD, LGD, and EAD. Section C, 
``Quantification: Special Cases and Applications,'' addresses a variety 
of special cases and applications of the retail quantification 
standards and procedures (for example, small business exposures, loan 
purchases, purchased retail receivables, and retail leases). Section D, 
``Validation,'' discusses how a bank should validate the segmentation 
and quantification processes.
    62. A number of general examples are given in the text of this 
chapter to aid exposition and interpretation. Some relevant 
implementation examples covering the four stages of the full 
quantification process are presented in ``Appendix A: Process Analysis 
Examples.'' The guidance concludes with a number of examples of 
technical issues specific to retail quantification in ``Appendix B: 
Technical Examples.''
The Four Stages of the Quantification Process
    63. Stage one--obtaining reference data. The bank assembles 
historical data that are used to estimate the retail IRB risk 
parameters. The reference data must closely resemble the bank's 
existing portfolio. Banks must use the best historical data available 
for quantifying

[[Page 62757]]

the retail IRB risk parameters. Over time, IRB banks will be expected 
to rely primarily on internal data for most of their retail portfolios, 
but supplemental external data may also be used when necessary. Banks 
may use more than one reference data set to improve the robustness or 
precision of the parameters. Reference data sets should include data on 
product type, borrower characteristics, and loan payment performance. 
Reference data for PD quantification includes some loans that later 
defaulted. Reference data sets for LGD and EAD quantification will 
consist solely of defaulted loans and the resulting recovery streams 
from internal historical data.
    64. Important considerations in the choice and construction of a 
reference data set include:
     Comparability of the reference data to the existing credit 
portfolio, including consistency of risk segmentation criteria, 
underwriting standards, and definitions of default and loss.
     The appropriate inclusion of periods of portfolio stress.
    65. The reference data set should also include the following:
     External factors relevant to the reference data that might 
affect the parameter estimates should be recorded, for example, the 
geographic concentration, the economic environment, and industry trends 
during the time period of the reference data.
     All borrower and loan characteristics that are used to 
estimate risk parameters must be included, as well as all variables 
necessary to redevelop and validate the estimation approach.
     The definition of default and methods of measuring loss 
that were in use at the time must be in the reference data set. The 
data must include collection costs, gain or loss on sale of collateral, 
date of default, etc.
    66. When it is not possible to use consistent segmentation criteria 
for both the reference and existing portfolio, reasonably close proxy 
characteristics must be found.
    67. Stage two--estimation. The bank applies analytical or 
statistical methods to the reference data to estimate a relationship 
between the borrower and loan risk characteristics embodied in the 
reference data and the outcomes of interest (defaults, loss severity, 
additional draws on unused lines prior to default). In other words, the 
bank uses empirical techniques to estimate the segment values of the 
risk parameters, PD, LGD, and EAD, as a function of the borrower and 
loan risk characteristics of the counterpart segment in the reference 
data. The risk parameter estimates may rely on relatively simple 
analysis of default rate or loss rate statistics from the reference 
data, or they may be a result of regression or other statistical 
estimation and classification techniques. This step may include 
adjustments for seasoning effects. A bank may use more than one 
technique to generate risk parameter estimates from the reference data 
if doing so improves robustness and accuracy of the estimates. If 
multiple estimates are generated, the bank must have a clear and 
consistent policy on reconciling the different estimates.
    68. Stage three--mapping. The bank establishes a close 
correspondence between the portfolio data and the reference data. The 
risk segmentation criteria for the reference data set should match 
closely the criteria for the existing portfolio. In addition, if any 
other characteristics of the reference data and the existing portfolio 
are used to estimate the risk parameters, they should correspond 
closely in both data sets. For many retail portfolios, mapping will be 
a relatively mechanical process for banks using quantitative criteria 
to segment and model risk. If the quantitative characteristics are 
equally valid and provide comparable measures, mapping will simply mean 
applying these characteristics to the existing portfolio. In some 
cases, mapping may be more challenging. For example, if a bank 
undertakes a major new effort to expand its offering of products on the 
Internet, and the bank has little internal data on exposures offered 
this way, the bank may need to augment its reference data with external 
data.
    69. Stage four--application. The bank applies the relationship 
estimated for the reference data to the actual portfolio data. The 
ultimate aim of quantification is to generate the risk parameter 
estimates for each segment of retail exposures within the existing 
portfolio. In the application stage, the bank often simply applies the 
risk parameter estimates calculated for each segment of retail 
exposures in the reference data to the corresponding segment in the 
existing portfolio. If the bank incorporates multiple data sets or 
estimation methods for the risk parameters, or if the mapping stage 
required adjustment to ensure a close match of the reference data and 
the existing portfolio data, the application stage could be more 
complex.
Integration of the Four Stages
    70. While the four-stage quantification described above is a useful 
conceptual approach, banks may satisfy supervisory standards without 
explicitly dividing the quantification process into four stages. In 
particular, the mapping and application stages may be fairly mechanical 
applications of the quantitative risk segmentation criteria to the 
existing portfolio. An example of a seamless approach to the four 
stages of quantification is provided in example 1 of appendix A.
    71. In general, the mapping and application stages will represent 
relatively straightforward processes when:
     The bank relies on quantitative segmentation criteria (for 
example, credit score, LTV, debt-to-income ratio), and these criteria 
represent relatively stable risk drivers over time. For example, if a 
bank uses a custom credit score, the score values must represent 
similar risk over the relevant time period.
     There are no major new product offerings, or changes in 
underwriting standards or other policies that require alternative risk 
segmentation criteria.
    72. The complexity of the mapping and application stages will 
depend on the availability of data and the consistency over time of 
factors such as product offerings and underwriting standards. For some 
banks or product types, it will be necessary to work through all four 
stages for one or more risk parameters. In those cases, a bank should 
use most or all of the detail, complexities, and contingencies 
concerning the mapping and application stages spelled out in the 
remainder of this chapter.
    73. Finally, while the four stages of quantification can sometimes 
be streamlined (because a bank's data history is extensive, for 
example), validation should not be streamlined. Even when a bank is 
able to take a straightforward approach, it must use the full 
validation process as prescribed in the last section of this chapter.
General Standards for Sound IRB Quantification
    74. Several core standards apply to all elements of the overall IRB 
retail quantification process; these general standards are discussed in 
this section. Other supervisory standards, specific to particular risk 
parameters, are discussed in the subsequent sections. When evaluating 
retail IRB quantification, supervisors will consider all of these 
standards, both general and specific. Particular practical approaches 
to retail quantification may be highly consistent with some standards 
and less so with others. In any particular case, an ultimate assessment 
relies on the judgment of supervisors to weigh the

[[Page 62758]]

strengths and weaknesses of a bank's chosen approach, using these 
supervisory standards as a guide.
    RS-11: Banks must have a fully specified process covering all 
aspects of retail quantification. The quantification process must be 
fully documented and updated periodically.
    75. A fully specified quantification process must describe how all 
four stages (data, estimation, mapping, and application) are 
implemented for each risk parameter. The quantification process should 
be periodically reviewed and updated to ensure that it incorporates new 
data, analytical techniques, and evolving industry practice.
    76. Documentation promotes consistency and allows third parties to 
review and replicate the entire process. Examples of third parties that 
might make use of the documentation include internal reviewers of the 
quantification model and risk segmentation system, internal validation 
teams within an independent function, and bank supervisors.
    77. Major decisions in the design and implementation of the 
quantification process should be justified and fully documented. A bank 
should have a well-defined policy for reviewing and updating the 
segmentation and quantification design. Particular attention should be 
given to new business lines or portfolios in which the distribution of 
retail exposures among segments is believed to have changed 
substantially. A material merger, the acquisition or sale of loans, and 
substantial account attrition or growth clearly raise questions about 
the continued applicability of the process and should trigger a review 
and possible updating.
    78. At a minimum, the risk parameter estimates must be updated at 
least quarterly and more frequently if deemed necessary for accurate 
credit risk management. New data should be incorporated into the risk 
parameter estimates using a well-defined process to correctly merge 
data sets over time. The frequency of updates and the process for doing 
so must be justified and documented in bank policy.
    79. The bank must ensure that the use of judgment in the design of 
the quantification system does not produce unduly low risk parameter 
estimates.
    80. Aspects of the quantification process that are apt to introduce 
greater uncertainty and potential error include the following:
     Uncertainty when there are substantial changes in the 
bank's product offerings, target customer base, or underwriting 
standards;
     Deficiencies or gaps in available data;
     The possibility of model error; and
     Mergers or acquisitions where the MIS for the acquired 
assets does not match the MIS for existing assets.
    81. The more uncertain the bank's estimates are as a result of any 
of the causes cited in the previous two paragraphs, the greater should 
be the margin of conservatism around those estimates, although these 
margins need not be added at each step.
    RS-12: Quantification must be based upon the best available data 
for the accurate estimation of IRB risk parameters.
    82. Given the bank-specific basis of assigning retail exposures to 
segments, over time banks are expected to regard internal data as the 
primary source of information for estimating IRB risk parameters. 
However, banks are permitted to use external data for quantification, 
provided a strong similarity can be demonstrated (1) between the bank's 
process of assigning exposures to a segment and the process used by the 
external data source, and (2) between the bank's internal credit risk 
profile for a given set of risk drivers and that of the external data.
    83. The bank must have a process for vetting potential reference 
data, whether the data are internal or external. The vetting should 
assess whether the data are sufficiently accurate, sufficiently 
complete, and sufficiently representative of the bank's existing 
exposures. Furthermore, the bank must have adequate data to estimate 
risk parameters for all loans on the books as if they were held to 
maturity, even if some loans are likely to be sold or securitized 
before their long-term credit performance can be observed. (See Section 
C, RS-27 of this chapter.)
    84. One objective of the IRB framework is to encourage further 
development of credit risk quantification techniques. Improving the 
quality, capture, and retention of internal data is an essential 
prerequisite for such advances.
    85. For new products it is likely that banks will need to 
supplement internal data with external sources. It may also be possible 
to accumulate internal data through the testing of new products by 
offering loans to a limited number of consumers and observing the 
performance.
    86. In the case of mergers or purchased portfolios, the data for 
the newly acquired segments may not be compatible with the purchasing 
bank's MIS. In such cases it may be necessary to gather data on 
borrower and loan characteristics from a combination of internal and 
external sources. Historical data on the purchased portfolios, if 
available from external sources, would allow the incorporation of 
borrower and loan risk characteristics data into the purchasing bank's 
internal database. The risk parameters can then be estimated by 
combining historical data from the purchased portfolio (if available) 
with internal reference data.
    87. Differences in economic conditions between the reference data's 
sample period and the present period should be monitored. In addition, 
the bank needs to consider any changes in trend behavior by consumers 
or small businesses that might affect the relevance of the historical 
data to the present period. For example, the bank may need to monitor 
actual or anticipated changes in consumer behavior due to changes in 
bankruptcy law or other factors.
    88. A well-defined and documented process should be in place to 
ensure that the reference data are updated as frequently as needed, as 
fresh data become available or as portfolio changes make necessary. All 
data sources, characteristics, and the overall processes governing data 
collection and maintenance must be fully documented, and that 
documentation should be readily available for review by supervisors.
    RS-13: The sample period for the reference data must be at least 
five years and must include periods of portfolio stress.
    89. In general, the bank should use all relevant historical data 
available, though the bank may weight some periods more heavily if it 
can demonstrate that the weighted data are likely to produce more 
accurate risk parameter estimates. Newer reference data, for example, 
may receive greater weight because of possible changes in bank 
products, underwriting standards, policies, and strategies. On the 
other hand, unusual recent circumstances in the bank's internal 
portfolio composition or in the historical period may make the recent 
data less applicable than the older data. If the reference data include 
data from beyond five years (to capture a period of stress or for other 
valid reasons), the reference data need not cover all of the 
intervening years.
    90. Example: During the 2001 to 2003 period of highly elevated 
mortgage prepayments owing to record low interest rates, losses may 
have been deferred in mortgage portfolios because of readily available 
refinancing options. Also, losses on foreclosures during this period 
were limited because housing prices generally increased throughout

[[Page 62759]]

the United States despite a recession. A similar (though not as 
substantial) drop in interest rates occurred in the early 1990s. That 
recession, however, was characterized by a sharp drop in property 
values in many parts of the country. In a case like this, where the 
recent period has been atypical, a bank may choose to weight the older 
data (perhaps from external sources) more heavily than the recent data.
    91. When a bank does not have sufficient historical data to 
encompass a period of stress for a particular portfolio, other sources 
of data covering stressed periods will be required. The bank may be 
able to select sub-samples of its internal portfolio that experienced 
stressed periods (for example, particular MSAs or geographic regions); 
see example 1 of appendix B. The bank may also use external data from 
industry sources.
    RS-14: Mapping must be based on a robust comparison of available 
data elements that are common to the existing portfolio and each 
reference data set.
    92. Sound mapping practice uses all key common elements available 
in the data. A mapping should be plausible and should be consistent 
with the risk segmentation system established by the bank. Levels and 
ranges of key characteristics for each segment of the existing 
portfolio's retail exposures should approximate the values of similar 
characteristics for the reference data.
    93. A bank that uses multiple reference data sets should conduct a 
rigorous mapping process for each data set. (Some common mapping 
challenges are discussed in example 2, appendix B.)
    94. The use of internal data for reference data purposes does not 
eliminate the need for a mapping requirement because changes in bank 
strategy (such as marketing, underwriting standards, or account 
management practices) or products may alter the risk characteristics or 
composition of the portfolio over time, even within the same pools of a 
risk segmentation system.
    RS-15: Mappings must be reviewed regularly and updated as 
necessary.
    95. Mappings should be reaffirmed regularly for both internal and 
external reference data, regardless of whether the risk segmentation 
criteria have undergone explicit changes during the period covered by 
the reference data set. Changes in borrower risk characteristics, 
products, and bank policies (for example, target population, 
underwriting standards, or collection policies) are quite typical in 
retail lines of business, so it is imperative that banks review all 
mappings regularly. When significant characteristics have been changed, 
added, or dropped, a new mapping must be established between the 
characteristics of the existing portfolio and characteristics of the 
reference data.
    RS-16: Banks that combine estimates from internal and external data 
or that use multiple estimation methods must have a clear policy 
governing the combination process and should examine the sensitivity of 
the results to alternative combinations.
    96. To improve the accuracy of its estimates, a bank might combine 
data from multiple sources and may use multiple estimation methods. The 
manner in which the estimates from multiple data sets or estimation 
methods are combined is extremely important, since different 
combinations will produce different parameter estimates for each 
segment. The bank should investigate parameter estimates' sensitivity 
to different ways of combining data sets or combining estimation 
methods. When results are highly sensitive to how data or estimates are 
combined, the bank should choose among the alternatives conservatively. 
A bank must document why it selected the combination techniques it did, 
and these techniques must be subject to appropriate approval and 
oversight by management.
    RS-17: A bank must have a clear, well-documented policy for 
addressing the absence of significant data elements in either the 
reference dataset or the existing portfolio.
    97. Some exposures in the reference data set and the existing 
portfolio will have missing data elements, some of which are important 
factors for measuring risk. Banks may segment these exposures 
separately for estimating the risk parameters. Alternatively, they may 
use a variety of statistical methods to impute values for the missing 
data points--provided these points can be sufficiently correlated to 
known information about the exposure. Expertise is required to judge 
whether such correlations can be established. Regardless of the 
approach and level of sophistication, the bank must have a clear and 
well-documented process describing how it treats missing data elements 
in the estimation and mapping stages.

B. Quantification of the IRB Risk Parameters

    RS-18: For estimating the IRB retail risk parameters, qualifying 
banks must use the IRB definition of default.
    98. For retail exposures, banks must use the following definition 
of default for IRB: A retail exposure will be considered in default for 
IRB purposes when any one of the following loss recognition events 
occurs:
     Loss recognition as embodied in the Federal Financial 
Institutions Examination Council (FFIEC) Uniform Retail Credit 
Classification and Account Management Policy. All residential mortgages 
and revolving credits must be recognized as defaults at 180 days past 
due, and all other retail loans must be recognized as defaults at 120 
days past due.
     A partial or full charge-off is taken against the 
exposure.
     The exposure is put on non-accrual status.
    99. For retail exposures (as opposed to wholesale exposures), the 
definition of default is applied to a particular loan rather than to 
the obligor. That is, default by an obligor on one obligation would not 
require a bank to treat all other obligations of the same obligor as 
defaulted.
    100. In the early stages of IRB implementation, a bank's historical 
reference data might not fully conform to the IRB definition of 
default. In addition, a bank may change its policies regarding charge-
offs or placing loans on non-accrual. In such cases, a bank should make 
conservative adjustments to reflect such discrepancies.
Quantification of Probability of Default (PD)
    101. For a given segment, the PD represents an estimate of the 
long-run average of one-year default rates. The one-year default rate 
(or default frequency) is the number of accounts that default at any 
time within a one-year period divided by the number of accounts open at 
the beginning of the year. (To figure in the calculation, an account 
must be open at the beginning of the period.) For unseasoned loans 
where seasoning effects are material, upward adjustments to the PD 
estimates will be necessary (as described in paragraphs 109 through 
112).
Data
    102. A bank must have a comprehensive reference data set that maps 
to the existing portfolio on a segment-by-segment basis. The same 
comparability standards apply to both internal and external data 
sources. All data sources must meet the minimum five-year requirement 
and include a period of economic stress. See example 4, appendix B for 
an example of a reference data set.

[[Page 62760]]

Estimation
    103. Estimation of PD is the process by which characteristics of 
the reference data are related to the default frequencies for each 
segment of exposures in the reference portfolio. The relevant 
characteristics that help to determine the PDs are referred to as 
``drivers of default.'' Drivers of default might include product, loan 
and borrower characteristics such as loan-to-value, credit line 
utilization, credit score, or delinquency status. Also, a portfolio 
separator such as geographic region, while not a direct driver of 
default, might indicate separate relationships by geographic region of 
the PD to these drivers. These drivers could be criteria for the 
assignment of exposures to pools in the risk segmentation system. A 
statistical model developed to estimate the PD would incorporate such 
drivers directly into the PD estimation.
    RS-19: Estimates of PD must be empirically based and must represent 
the average over time of segment default frequencies on an account 
basis. The effects of seasoning, prepayments, and attrition must be 
considered in the PD estimates.
    104. PD estimates should capture average expected default rates for 
a segment given its risk characteristics. PD estimates should represent 
averages of default rates measured over a sufficiently long time period 
to provide accurate estimates. The estimation period must include 
periods of economic distress.
    105. When estimating PDs, a bank may give equal weight to each 
sample period or it may weight recent data more heavily if it can 
demonstrate that doing so is more predictive of future default 
behavior.
    106. If the bank calculates an average PD over time by weighting 
each year's segment-level PD by the number of loans or volume of 
outstanding balances, the estimated PD may be lower or higher than the 
estimated PD from an unweighted average. For example, if lending 
typically declines during periods of stress, this weighting will tend 
to lessen the impact of the stress periods on the weighted average. A 
bank using such an approach would be expected to empirically 
demonstrate that such an approach produces a more accurate estimated PD 
for its existing portfolio. See example 2 of appendix A for an example 
of the quantification of a models-based PD consistent with a long-run 
average.
    107. Different methods of measuring and tracking exposures, 
defaults, and losses are common in credit risk management. Banks are 
required to produce an estimate equivalent to the one-year account 
default rate. See example 3 in appendix B.
    108. Some banks may choose to derive a PD based on the average 
expected dollar loss rate. A bank may use this method as long as it 
produces an accurate PD on an account basis as defined in paragraph 
101. See example 3 in appendix A.
Seasoning
    109. Seasoning poses a challenge for banks quantifying the default 
rate for retail exposures when the default rate follows a 
characteristic account age profile, typically rising for the first 
several periods following origination and then falling. Seasoning is an 
issue for longer-maturity consumer products such as residential 
mortgages, but it may also be important for shorter-lived portfolios. 
In addition, accounting for seasoning is particularly significant for 
portfolios that are growing rapidly through new originations or for 
banks that systematically sell or securitize loans before they reach 
the peak of the seasoning curve. In both cases, banks should factor 
seasoning into their quantification to provide adequate capital to 
cover future needs.
    110. For segments containing unseasoned loans, a bank should assign 
a higher PD estimate that reflects the annualized cumulative default 
rate over the segments' expected remaining life.\8\ For seasoned loans, 
the bank should use the long-run average of one-year PDs.
---------------------------------------------------------------------------

    \8\ If the bank can demonstrate that seasoning does not have a 
material effect on PD, the bank can use the long-run average of one-
year PDs.
---------------------------------------------------------------------------

    111. The account age profile may be tracked by using account age as 
a criterion in the risk segmentation system or as a predictive variable 
of the PD parameter. Several methods can be used to account for 
seasoning in the PD estimates. See example 4 in appendix A.
    112. Periods of unusual prepayments or other types of account 
attrition have the potential to materially alter the estimated 
historical default rates for some retail exposures. PD estimates must 
be developed in such a way that they are not distorted by periods of 
unusual prepayment activity or other types of account attrition in the 
reference data sets.
Mapping
    113. Mapping is establishing a correspondence between the existing 
portfolio and the reference data--that is, it is identifying how the 
existing portfolio's product, loan, and borrower risk characteristics 
relate to the reference data's characteristics. Mapping enables a bank 
to determine how risk parameter estimates from the reference data 
should apply to the existing portfolio. For banks with a consistent, 
long-term process of risk segmentation, PD mapping may consist simply 
of adopting the long-run average PD estimates from the historical data. 
However, if the bank's internal risk segmentation has varied over time, 
the bank must demonstrate a discernable link between its existing 
segmentation system and the long-run PD estimates produced from the 
reference data.
    114. In some business lines, products, or cross-sections of the 
portfolio, certain drivers of default may not be available in the risk 
segmentation system. Drivers are most likely to be missing as banks 
transition to an IRB system or when a bank acquires a portfolio. In 
such cases, the bank should modify its mapping process accordingly. 
Supervisors expect this practice to be temporary, however, and as the 
requisite data become available, banks should incorporate the omitted 
effects into the risk segmentation system.
Application
    115. In the application stage, the bank applies the PD estimates to 
the risk segments of the existing portfolio to calculate minimum 
regulatory capital. This should be a relatively mechanical process for 
most retail portfolios.
    RS-20: PD estimates for all retail segments cannot be less than 
0.03 percent (3 basis points)
Quantification of Loss Given Default (LGD)
    116. LGD is defined as the segment's credit-related economic losses 
net of discounted recoveries divided by the segment's exposure at 
default, all measured during a period of high credit losses for the 
particular portfolio (e.g., mortgages, credit cards). The LGD 
estimation process is similar to the PD estimation process. The bank 
identifies a reference data set, which must include periods of 
portfolio stress. Once the bank obtains these data sets, it should 
select a technique to estimate the credit-related economic loss per 
dollar of exposure for all defaulted loans in each reference segment. 
The bank's reference data should then be mapped to the bank's existing 
retail segments, so that the model can be applied to generate an 
estimate of the LGD for each segment in the existing portfolio.
Data
    117. Unlike reference data sets used for PD estimation, data sets 
for LGD

[[Page 62761]]

estimation contain only defaulted exposures.
    118. In order to calculate economic loss, the reference data sets 
must include all relevant data for quantifying LGD. This would include 
the exposure at the time of default (including principal plus unpaid 
but capitalized interest and fees), recoveries, and material collection 
and workout expenses. The data should contain the circumstances of 
default, for example, roll to charge-off or bankruptcy leading to 
charge-off, if they are significant factors for LGD. Recovery data 
should include the income and timing of recoveries including direct 
payments from the consumer, the sale of the collateral, or realized 
income from the sale of defaulted loans. For defaulted loans and 
collateral still on the balance sheet, the estimated current market 
value can be used to proxy the recovery amount. Cost data comprise the 
material direct and indirect costs associated with workouts and 
collections, including the dates when the various costs were incurred.
    119. The same minimum history of five years for the LGD reference 
data set is required, or longer to include a period of portfolio 
stress. Although a bank may use internal or external data, most banks 
will eventually be expected to collect and maintain sufficient internal 
data.
    120. In the LGD calculation, all material credit-related losses 
must be captured, whether or not those losses are ultimately charged to 
the ALLL. Material credit-related losses are broadly defined to include 
any material losses associated with a defaulted loan, including write-
off of unpaid interest or fees, write-downs of repossessed collateral, 
and any similar losses.
Estimation
    121. Banks must determine an accurate LGD parameter for each 
segment. As discussed in chapter II, banks may estimate and apply a 
common LGD over a range of risk segments within a particular product 
type, where appropriate.
    RS-21: The estimates of LGD must reflect the concept of ``economic 
loss.''
    122. For estimating LGD, the definition of loss is based on the 
concept of economic loss, which is a broader, more inclusive concept 
than accounting measures of loss. Economic loss incorporates the mark-
to-market loss of value of the defaulted loan and collateral plus all 
direct and indirect costs of workout and collections, net of recoveries 
(including late fees and interest). Losses, recoveries, and costs 
should all be discounted to the time of default.
    123. The scope of cash flows included in recoveries and costs is 
meant to be broad. Workout and collection costs that can be clearly 
attributed to certain segments of loans, plus indirect cost items, must 
be reflected in the bank's LGD assignments for those exposures. 
Recovery costs include the costs of running the bank's collection and 
workout departments and the cost of outsourced collection services 
directly attributable to recoveries during a particular time or for a 
particular segment of loans, at as granular a level as possible. 
Recovery costs also include an appropriate percentage of other ongoing 
costs, such as corporate overhead.
    124. These recovery costs can be allocated using the same 
principles and techniques of cost accounting that are usually used to 
determine the profit and loss of activities within any large 
enterprise. Collection and workout departments, however, may cover 
services not 100 percent attributable to defaulted loans. For example, 
the same call center may manage reminder calls to delinquent accounts, 
many of which will never default, as well as collection calls. The 
expenses for these functions should be differentiated to allocate only 
collection expenses attributable to defaulted loans.
    125. When costs can't be allocated because of data limitations, the 
bank may assign those costs using broad averages. (For example, the 
bank could allocate costs by outstanding dollar amounts of loans, 
including unpaid interest and fees at the time of default, within each 
segment.)
    126. All losses, costs, and recoveries should be discounted to the 
time of default if realization of those material costs and recoveries 
is significantly delayed. The discount rate should be applied to the 
time interval between the date of default and the date of the realized 
loss, incurred cost, or recovery, on a pooled basis. A bank must 
establish a discount rate that reflects the time value of money and the 
opportunity cost of funds to apply to recoveries and costs. The 
discount rate, which should reflect the distressed nature of the asset, 
should usually exceed the contract interest rate for newly originated 
products as of the date of default. Within the retail portfolio, the 
discounting process will be particularly important in the case of 
residential mortgages because foreclosure laws in many states allow 
considerable time to pass between default and recovery.
    RS-22: The estimated LGD must reflect loss severities during 
periods of high credit losses.
    127. A bank must estimate an LGD for each segment that reflects 
economic downturn conditions where necessary to capture the relevant 
risks. The LGD cannot be less than the long-run default-weighted 
average LGD calculated on the basis of the average economic loss of all 
observed defaults within the data source for that retail segment. In 
addition, a bank must take into account the potential for the LGD to be 
higher than the default-weighted average during a period when credit 
losses for a particular portfolio (e.g., mortgages) are substantially 
higher than average. For certain types of exposures, loss severities 
may not exhibit such cyclical variability, and LGD estimates may not 
differ materially (or possibly at all) from the long-run default-
weighted average. However, for other exposures, this cyclical 
variability in loss severities may be significant, and banks will need 
to incorporate it into their LGD estimates. For this purpose, banks may 
use averages of loss severities observed during periods of high credit 
losses for that product, forecasts based on appropriately conservative 
assumptions, or other similar methods.
    128. The LGD of an asset does not change with its actual default. 
The assigned LGD should already reflect a default loss experience 
predicated on a period of high credit losses. However, once an asset 
actually defaults, the bank must construct its best estimate of 
expected losses for it based on current economic circumstances and risk 
characteristics. For this purpose, banks can group defaulted loans into 
segments. (See chapter II.) The amount, if any, by which the LGD on the 
defaulted asset segment exceeds the bank's best estimate of the current 
expected loss rate on the segment represents the capital requirement 
(K) for that segment. The agencies are considering the possible 
establishment of an appropriate capital requirement floor for defaulted 
assets. When the best estimate of expected loss on a defaulted asset is 
less than the sum of specific provisions and partial charge-offs, that 
asset will attract supervisory scrutiny and must be justified by the 
bank.
    129. Examples 5, 6, and 7 in appendix B present some issues related 
to LGD estimation.
Mapping
    130. LGD mapping follows the same general standards as PD mapping. 
The default and loss definitions and loss severity risk drivers in the 
reference data and the existing portfolio of retail exposures must be 
comparable. Some common challenges in mapping are presented in example 
2, appendix B.

[[Page 62762]]

The mapping process must be updated regularly, well documented, and 
independently reviewed.
Application
    131. At the application stage, banks apply the LGD estimation 
framework to their existing portfolio of exposures. Doing so might 
require banks to aggregate individual segment-level LGD estimates into 
broader averages or to combine estimates.
    132. LGD may be particularly sensitive to changes in the way banks 
manage retail credits. For example, a change in policy regarding 
collection practices or loan sales may have a significant impact on the 
quantification of LGD. When such changes take place, the bank should 
consider them in all steps of the quantification process. If a bank's 
policy changes seem likely to reduce LGD, estimates should be reduced 
only after the bank accumulates a significant amount of actual 
experience under the new policy to support the reductions; on the other 
hand, policy changes that are likely to increase LGD should be 
reflected in the estimates in a timely fashion.
    RS-23: IRB banks have a minimum LGD of 10 percent for residential 
mortgages.
    133. This floor is based on the view that LGDs, if appropriately 
estimated, are unlikely to fall below this level during periods of high 
credit losses. During the initial two-year implementation period of the 
IRB framework, the LGDs for retail residential mortgages cannot be set 
below 10 percent. During this transition period, the agencies will 
review the potential need for continuation of this floor. Mortgages 
guaranteed by a sovereign government are exempt from this floor.\9\
---------------------------------------------------------------------------

    \9\ This exemption applies to VA-guaranteed and FHA-insured 
mortgages.
---------------------------------------------------------------------------

    RS-24: If banks choose to reflect the risk-mitigating effect of 
private mortgage insurance (PMI) for residential mortgages in their 
risk estimates, they must do so by incorporating these insurance 
benefits into the quantification of segment-level LGD.
    134. In calculating losses for LGD estimation, the amount of 
expected PMI benefits would be deducted from the losses otherwise 
incurred by the bank on defaulted mortgages.
    135. Banks may choose to incorporate loan-level PMI coverage into 
their risk segmentation. For example, loans with similar risk 
characteristics, including the same type of PMI coverage, could be 
placed in a single segment. In any case, banks will need accurate PMI 
coverage data in both the reference and existing-portfolio data sets. 
This would generally require loan-by-loan tracking of PMI over the life 
of the loan, since loans on which the lender requires PMI coverage at 
origination (generally because of LTVs greater than 80 percent) often 
drop coverage when current LTV falls below 80 percent. Pool-level 
mortgage insurance is treated under the IRB securitization framework or 
under the general IRB credit risk mitigation rules.
    136. Banks with substantial PMI-covered residential mortgages 
should monitor the senior unsecured debt ratings of the PMI companies. 
If the rating of any PMI company falls below AA, banks should 
accordingly adjust the LGD to take into account the elevated 
counterparty risk for all mortgages insured by that company.
Quantification of Exposure at Default (EAD)
Introduction
    RS-25: The bank must provide an estimate of EAD for each segment in 
its retail portfolio.
    137. For an individual retail exposure, EAD is the gross amount due 
at default, which is the amount by which regulatory capital would be 
reduced if the exposure were to be fully written off. This includes all 
accrued, but unpaid, interest and fees. EAD for defaulted assets 
includes any partial write-offs that have already been incurred. EAD 
for a segment is the sum of the EADs of all the loans in the segment. 
For fixed exposures such as term loans and installment loans, each 
loan's EAD is no less than the principal balance outstanding.\10\ For 
revolving exposures and other lines of credit such as credit cards, 
overdrafts on checking accounts, and home equity lines of credit, each 
loan's EAD includes the outstanding balance plus estimated net 
additions to balances for loans defaulting over the following year. 
These additions consist of future principal increases including 
capitalized future interest and fees.
---------------------------------------------------------------------------

    \10\ For all loans, the LGD calculation includes all unpaid 
interest and fees in the measure of economic loss.
---------------------------------------------------------------------------

    138. For purchased loans, the EAD is set equal to the purchase 
price. For example, if a bank buys a retail portfolio consisting of 
exposures with $100 million face value at a 5 percent discount, the 
initial EAD for the purchasing bank is $95 million. (Example 8 in 
appendix B illustrates the effect of the purchase discount on EAD and 
LGD.)
    139. To estimate the net additional draws, many banks estimate a 
loan equivalent exposure (LEQ) as the percentage of the total 
authorized but undrawn lines expected to be drawn down by borrowers 
that default. Thus, the estimated dollar value of the additional 
drawdown before default can be represented as:

Net additional draws =

LEQ * (total authorized line - present outstanding balance)

    EAD for the segment can then be represented as:

EAD = Present outstandings + Net additional draws

    It is the LEQ that must be estimated, since the total authorized 
line and the amount presently outstanding are known. The estimation of 
the LEQ is the focus of this section of the guidance.
    140. A bank quantifies its EAD by working through the four stages 
of quantification: the bank must develop a reference data set; it must 
estimate an EAD for segments in the reference data set with a given 
array of characteristics; it must map its existing portfolio to the 
reference data; and by applying the mapping, it must generate an EAD 
estimate for each segment in the existing portfolio.
Data
    141. In order to estimate LEQ for an entire segment, EAD reference 
data sets contain only defaulted loans. In many cases, the same 
reference data may be used for both LGD and EAD. In addition to 
relevant descriptive characteristics that can be used in estimation, 
the reference data must include historical information on drawn and 
undrawn exposures prior to default, as well as the drawn exposure at 
the date of default.
    142. As discussed below under ``Estimation,'' LEQ estimates of 
potential draws may be developed using either a cohort method or a 
fixed-horizon method. The bank's reference data set should be 
structured so that it is consistent with the estimation method that the 
bank applies.
Estimation
    143. To derive LEQ estimates for each segment, characteristics of 
the reference data are related to additional drawings preceding a 
default event. The estimation process should be capable of producing an 
average estimate of draws on unused lines to support the EAD 
calculation for each segment. Two broad types of estimation methods are 
used in practice: the cohort method and the fixed-horizon method. 
Regardless of the method used, the LEQ estimates must accurately 
capture the potential exposure to losses from loans defaulting over the 
coming year.

[[Page 62763]]

    144. Under the cohort method, a bank groups defaults into discrete 
calendar periods, typically one year. A bank may use a longer period if 
it provides a more accurate estimate of total future losses arising 
from undrawn exposures. The bank then estimates the relationship 
between the balances for defaulted loans at the start of the calendar 
period and the balances at the time of default.
    145. Under the fixed-horizon method, the bank bases its estimates 
on a reference data set that supplies the actual exposure at default 
for each defaulted loan along with the drawn and undrawn amounts at a 
fixed interval prior to default. Estimates of LEQ are computed from the 
increase in balances that occur over the fixed-horizon interval for the 
defaults in the segment. The time interval used for the fixed-horizon 
method must be sufficiently long to capture the additional exposures 
generated by loans that default during the year for which the risk 
parameters are being estimated. In particular, the appropriate fixed 
interval will be influenced by charge-off policies. For example, using 
a six-month time interval for credit card loans would underestimate 
EAD.
    RS-26: The estimated LEQ must reflect estimated net additional 
draws during periods of high credit losses.
    146. The LEQ cannot be less than the long-run default-weighted 
average for that retail segment. The LEQ must reflect net additional 
draws observed during periods of high credit losses if these are 
systematically higher than the default-weighted average. For this 
purpose, banks may use averages of LEQs observed during periods of high 
credit losses for that product, forecasts based on appropriately 
conservative assumptions, or other similar methods.
Mapping
    147. If the characteristics that drive EAD in the reference data 
are the same as those used for the risk segmentation system of the 
bank's existing retail portfolio, mapping may be relatively 
straightforward. However, if the relevant characteristics are not 
available in a bank's existing portfolio risk segmentation system, the 
bank will encounter the same mapping complexities that it does when 
mapping PD and LGD in similar circumstances.
Application
    148. In the application stage, the estimated relationship between 
risk drivers and LEQ is applied to the bank's existing portfolio. With 
the exception of portfolios purchased at a discount, the estimated EAD 
must be at least as large as the currently drawn amount in each 
segment; therefore, LEQs cannot be negative. Multiple reference data 
sets may be used for LEQ estimation and combined at the application 
stage, subject to the general standards for using multiple data sets.
    149. EAD may be particularly sensitive to changes in the way banks 
manage retail credits. For example, a change in policy regarding line 
increases or decreases for particular segments may have a significant 
impact on LEQ. When such changes take place, the bank should consider 
them when making its estimates--and it should do so from a conservative 
point of view. Policy changes likely to significantly increase LEQ 
should prompt immediate increases in LEQ estimates. If a bank's policy 
changes seem likely to reduce LEQ, estimates should be reduced only 
after the bank accumulates a significant amount of actual experience 
under the new policy to support the reductions.

C. Quantification: Special Cases and Applications

Small Business Exposures
    150. Certain exposures to a company or to an individual for 
business purposes can be included in the ``other retail'' category for 
IRB purposes provided they meet the following conditions:
     A small business loan must be managed by the bank on a 
segmented basis, where credit scoring is often a key component of the 
underwriting decision process, and the bank must estimate risk 
parameters for segments of such loans with similar risk 
characteristics. (If the small business exposures are rated and managed 
as individual exposures, they will fall under the corporate standards 
and requirements.)
     The total of all of the bank's exposures to a single 
business (whether in the name of the business or in the name(s) of the 
proprietor(s) for business purposes) cannot exceed $1 million.
     Revolving exposures to an individual can be treated as 
QREs, even if used for business purposes. However, revolving exposures 
to businesses will be treated as ``other retail'' if they meet the 
criteria above.
    151. Small business exposures qualifying for retail treatment are 
subject to all the standards applicable to other retail exposures.
Retail Leases
    152. The minimum capital requirement for retail leases is the sum 
of (1) the credit risk capital requirement on the discounted lease 
payment stream plus (2) 8% of the residual value of the leased asset:
     The lease payment credit risk is determined by estimating 
PD and LGD in the same manner as retail loan exposures; EAD equals the 
discounted remaining lease payment stream.
     The risk of the residual value is the bank's exposure to 
loss arising from potential decline in the fair value of the leased 
asset below the estimate at the time of lease inception.
Purchased Retail Receivables
    153. Purchased retail receivables are treated the same as other 
categories of retail exposures, except for the effects of dilution. 
Dilution effects refer to the potential reduction in receivable 
balances caused by cash or non-cash credits granted to the receivables' 
obligor(s). Examples include offsets for the return of goods sold and 
discounts given for prompt payment. If dilution poses a material risk, 
banks should estimate an expected (long-run average) one-year dilution 
rate (as a percentage of the receivables amount.) The minimum 
regulatory capital requirement for dilution risk is determined 
according to the corporate risk weight formula.
    154. When refundable purchase price discounts, collateral, or 
partial guarantees provide first dollar loss protection for purchased 
retail receivables, banks may treat these as first dollar loss 
protection under the IRB securitization framework and use that 
framework for the calculation of minimum capital requirements for the 
purchased retail receivables. Alternatively, the bank may choose to 
treat EAD as the purchase price.
Loan Sales
    RS-27: Quantification of the IRB risk parameters must be adjusted 
appropriately to recognize the risk characteristics of exposures that 
were removed from reference data sets through loan sales or 
securitizations.
    155. Banks must estimate the risk parameters for all loans on the 
books as if they were held to maturity, even if some loans are likely 
to be sold or securitized before their long-term credit performance can 
be observed. Loan sales and securitizations, however, can pose 
substantial difficulties for quantification. For example, PDs might 
appear disproportionately low if loans are sold before their historical 
performance patterns become manifest. Adjusting the risk parameter 
estimation to correct for sales or securitization would be particularly 
important for a bank that sells off primarily credits that are 
performing poorly (for example, delinquent loans).
    156. If the potential bias in the parameter estimates created by 
loan

[[Page 62764]]

sales and securitizations is material, the bank must identify, by 
detailed risk characteristics, the loans sold out of the pool or 
portfolio when using internal historical data as reference data sets 
for quantification purposes.
    157. For banks with a history of regularly selling or securitizing 
loans of particular types, long-run performance data should be 
available from the servicers or trustees. Alternatively, banks may be 
able to construct appropriate reference data sets with risk 
characteristics comparable to the loans sold or securitized by using 
internal historical data from retained pools or external data.
Securitization and Undrawn Balances
    158. For QREs, home equity lines of credit (HELOCs), and other 
retail products where the drawn balances of certain accounts in the 
portfolio have been securitized, the IRB risk parameters and minimum 
capital requirements shall be determined as follows:
     For the seller's interest in securitized receivables, the 
risk parameters and minimum capital requirements must be determined as 
stipulated in this chapter.
     The potential additions to the balances prior to default 
for all of the accounts with securitized balances must be determined in 
accordance with the section of this chapter on EAD. These additions 
must be allocated between the seller's (originating bank's) and 
investors' shares on a pro rata basis, in the same proportions as the 
drawn balances of the accounts.
     For the seller's interest in the undrawn balances, the 
risk parameters and capital requirements must be determined as 
stipulated in this chapter.
     For the investors' interest in the undrawn balances, 
minimum regulatory capital will be determined according to the IRB 
rules for securitizations.
Multiple Legal Entities
    159. In those cases where quantification is conducted across 
portfolios that are held by two or more legal entities, segmentation 
must meet all the standards set forth in Chapter II. Exposures assigned 
to a single segment must share homogeneous risk characteristics, 
regardless of whether the exposures are held on the books of a single 
or multiple legal entities, to ensure that the risk parameters 
accurately reflect the risk of the exposures held by that entity. For 
example, if a particular institution within the banking group holds 
loans with unique or predictive characteristics (such as credit card 
loans originated through a specific marketing channel or mortgage loans 
in a certain location), the segmentation system must be designed to 
incorporate these characteristics to ensure that PDs, LGDs and EADs for 
each entity are accurately stated. The following standards also apply:
     The risk parameters for each segment are determined on a 
segment-wide basis in the same manner described in the preceding 
sections of this chapter.
     Capital requirements for each legal entity should be based 
on the pro-rata share of the EAD exposure for each segment that is 
owned by that entity.
     Periodic validation should be conducted to confirm that 
minimum capital requirements determined through this approach are not 
materially different from those that would be determined on a separate 
entity basis.
QRE Treatment Qualification
    160. To qualify for QRE treatment, in addition to the other 
requirements listed in chapter I, banks must demonstrate that their 
revolving portfolios are characterized by low volatility of loss rates 
relative to average loss rates, particularly for low PD bands.
    161. Specifically, [sigma]LR/LR must be ``relatively 
low,'' where LR is the average loss rate, and [sigma]LR is 
the volatility, or the standard deviation of the average loss rate over 
time.
    162. The average loss rate and the standard deviation should be 
calculated over a sufficiently long time period to be representative of 
the performance of the portfolio over both good and stressful economic 
environments.
    163. There is no fixed threshold for what constitutes a ``low 
ratio'' of [sigma]LR to LR. Banks will be expected to 
develop and document policies for their thresholds, and to compare 
ratios across portfolios that meet all the remaining qualifications for 
QRE treatment. In addition, they should compare the ratios to those of 
their other retail portfolios and their corporate and bank portfolios. 
Banks must retain data on their loss rates.
    164. If the ratio of loss rate volatility to average loss rates is 
not sufficiently low, the portfolio will be subject to treatment as 
``other retail'' rather than as QRE. Supervisors will review the 
relative volatility of loss rates across the QRE sub-portfolios, as 
well as the aggregate QRE portfolio, and intend to share information on 
the typical characteristics of QRE loss rates across jurisdictions.
Stress Testing
    165. Stress-testing analysis indicates the effect of economic 
downturns on credit quality and the resulting effect on capital 
requirements. Under the new framework, changes in borrower credit 
quality will lead to changes in the required IRB regulatory capital 
charge. Since credit quality changes typically reflect changing 
economic conditions, required regulatory capital may also vary with the 
economic cycle. During an economic downturn, regulatory capital 
requirements could increase if exposures migrate toward lower credit 
quality segments as a result of higher unemployment and lower incomes.
    166. Supervisors expect that banks will manage their regulatory 
capital position so that they remain adequately capitalized during all 
phases of the economic cycle. A bank that is able to credibly estimate 
regulatory capital levels during a downturn can be more confident of 
appropriately managing regulatory capital. Stress testing is one tool 
for that estimation, by means of projecting the levels of key 
performance measures in an economic downturn.
    167. Stress testing is a general term that can be applied to 
different types of analysis, depending on the purpose of the exercise. 
To cite an example that differs from the type of stress testing 
considered here, a bank might want to shed light on how it would fare 
during an extreme scenario that threatens its continued existence. 
Still another type of stress testing evaluates the effect of an adverse 
scenario (such as a significant increase in unemployment) on the credit 
quality of a group of borrowers.
    168. Banks are encouraged to use a range of scenarios when stress 
testing to manage regulatory capital. Scenarios may be historical, 
judgmental, or model-based. Key variables specified in a scenario could 
include interest rates, score-band segment transition matrices, asset 
values, growth rates, and unemployment rates. A bank may choose to have 
a single scenario that applies to the entire portfolio, or it may 
identify scenarios specific to the various portfolio segments. The 
severity of the stress scenario should be consistent with the periodic 
economic downturns experienced in the United States. Such scenarios may 
be less severe than those used for other purposes, such as testing a 
bank's solvency.
    169. Given a scenario, a bank then estimates the effect of the 
scenario on risk-weighted assets and its future capital ratios relative 
to the regulatory minimums. Estimating capital ratios includes 
estimating levels of capital (the numerator of the ratio) as well as 
measures of risk-weighted assets (the denominator). Suppose the 
scenario for a large retail portfolio segment is a specific historical 
recession (for

[[Page 62765]]

example, the national unemployment rates of 1980 to 1985). Score-band 
transition matrices observed during the recession could be used to 
quantify migration between segments and thus supply the new 
distribution of segments expected for the current portfolio, given the 
scenario. This would allow the calculation of risk-weighted assets that 
would be expected in the recession scenario. Default rates would allow 
the estimation of the effects on bank income and the consequent capital 
effects of credit losses.
    170. The scope of this estimation exercise should be broad and 
include all material portfolios under IRB. The time horizon of the 
stress-testing analysis should be consistent with the specifics of the 
scenario and should be long enough to measure the material effects of 
the scenario on key performance measures. For example, if a scenario 
such as a historical recession has material income and segment 
migration effects over two years, the appropriate time horizon is at 
least two years.
    171. The stress-testing exercise should also take into account a 
bank's discretionary actions that affect regulatory capital levels. For 
example, a bank's plan to reduce dividends in the face of lowered 
income would, if implemented, affect retained earnings and the capital 
accounts. Holding more than the minimum regulatory capital requirements 
during normal economic conditions is a key discretionary action. Such 
discretionary actions must be consistent with the bank's documented 
regulatory capital management policy. Because discretionary plans may 
or may not be implemented, a bank should estimate the relevant capital 
ratios both with and without these actions.

D. Validation

Introduction
    172. Validation consists of a wide range of activities intended to 
assure that the risk segmentation method and the risk quantification 
process are logical and sound and that the segment-level forecasts of 
PD, LGD and EAD are accurate.
    173. It is often rather difficult to disentangle the effects of the 
risk segmentation system from those of the quantification process, in 
particular with respect to validation. Some aspects of the validation 
of the risk segmentation system can be assessed independently; those 
have been discussed in chapter II. However, to a very significant 
degree, the accuracy, logic, and statistical powers of the segmentation 
system are inextricably intertwined with the accuracy and validity of 
the risk parameters estimated on the basis of that segmentation. 
Therefore, most of the discussion that follows applies to both the risk 
segmentation system and the risk parameter quantification process.
    174. The units that develop and test the segmentation and 
quantification processes should conduct the types of ongoing validation 
discussed below. In addition, there must be independent review 
conducted by a separate unit. See chapter V for details.
    RS-28: A validation process must cover all aspects of IRB retail 
quantification.
    175. Validation of the risk quantification process should focus on 
the three estimated segment-level retail IRB parameters, PD, LGD, and 
EAD. Although the established validation process should result in an 
overall assessment of IRB quantification for each parameter, it also 
must cover each of the four stages of the quantification process as 
described in preceding sections of this chapter (data, estimation, 
mapping, and application). Validation of the risk segmentation system 
should focus on the design and the ongoing ability of the system to 
divide exposures into stable and homogeneous segments that separate 
exposures effectively by risk. The process must be updated periodically 
to incorporate new developments in validation practices and to ensure 
that validation methods remain appropriate. Documentation must be 
updated whenever validation methods change.
    RS-29: A bank must establish policies for all aspects of 
validation. A bank must comprehensively validate risk segmentation and 
quantification at least annually, document the results, and report its 
findings to senior management.
    176. A full and comprehensive annual validation is a minimum for 
effective risk management under IRB. More frequent validation may be 
appropriate for certain parts of the IRB system and in certain 
circumstances; for example, during high-default periods, banks should 
compute realized default and loss severity rates more frequently. They 
must document the results of validation and report them to appropriate 
levels of senior risk management.
    RS-30: Banks must use a variety of validation approaches or tools; 
no single validation tool can completely and conclusively assess IRB 
quantification. A bank's validation processes must include the 
evaluation of logic, ongoing monitoring, and the comparison of 
estimated parameter values with actual outcomes.
    177. Banks must have processes designed to give reasonable 
assurances of their quantification systems' accuracy. The ongoing 
process to confirm and ensure accuracy consists of:
     The evaluation of developmental evidence (evaluation of 
logic) or the evaluation of the conceptual soundness of the approach to 
quantification;
     Ongoing monitoring of system implementation and 
reasonableness (verification and benchmarking); and
     Back-testing (comparing actual with predicted outcomes).
    178. IRB banks are expected to employ all of the components of this 
process. However, the data to perform comprehensive back-testing may 
not be available in the early stages of implementing an IRB 
segmentation and quantification process. In addition, back-testing may 
be difficult if a bank's process for modeling risks is evolving 
significantly. Therefore, banks may at times need to rely more heavily 
on developmental evidence, quality control tests, and benchmarking to 
assure themselves and other interested parties that their 
quantification processes are likely to be accurate.
Developmental Evidence
    RS-31: Banks must evaluate the developmental evidence, or logic, 
involved with the development of the risk segmentation system and the 
quantification process.
    179. Evaluating logic is essential in validating the risk 
segmentation system and all four stages of the quantification process. 
Developing a risk segmentation system and quantification process 
requires banks to adopt methods, choose characteristics, and make 
adjustments; each of these actions requires judgment. Validation should 
ensure that these judgments are plausible and informed and that they 
reflect as much as possible evolving industry practice and the latest 
theoretical developments and empirical techniques in the risk 
management field.
    180. Evaluating developmental evidence involves making a reasonable 
assessment of the quality of the quantification process by analyzing 
the design and construction of the four stages of quantification. 
Developmental evidence is intended to answer these questions: Could the 
risk segmentation system be expected to accurately measure the risk 
within each segment and to separate the risk between segments? Could 
the quantification process be expected to accurately estimate PDs, 
LGDs, and EADs? That evidence will have to be revisited whenever the 
bank changes its quantification process or its risk

[[Page 62766]]

segmentation system. Since risk analysis at advanced banks is 
constantly evolving, the evaluation of developmental evidence is likely 
to be an important ongoing part of the process.
    181. Generally, the evaluation of developmental evidence will 
include a body of expert opinion. Developmental evidence in support of 
the risk segmentation system includes the statistical design of the 
segmentation in separating exposures into stable and homogeneous 
segments and the selection and combination of default risk drivers. 
Developmental evidence in support of techniques used in the 
quantification process must include information on the logic that 
supports the methods chosen for the four stages of quantification. The 
developmental evidence will be more persuasive when it includes 
empirical evidence on the power of the segmentation system to separate 
exposures by risk and the accuracy of the quantification process. The 
sufficiency of the developmental evidence will itself be a matter of 
informed expert opinion, and experts should be able to draw conclusions 
about whether an IRB system would be likely to perform satisfactorily.
Ongoing Process Verification and Benchmarking
    RS-32: Banks must conduct ongoing process verification on the 
developed risk segmentation system and quantification process to ensure 
proper implementation.
    182. The second source of analytical support for the validity of a 
bank's IRB systems is the ongoing analysis to confirm that the process 
continues to perform as intended. Such analysis involves process 
verification and benchmarking.
    183. Verification activities address the question: Are methods of 
separating exposures into segments and quantifying risk parameters 
being used, monitored, and updated as designed?
    184. Risk segmentation and quantification process verification also 
includes monitoring of model overrides. If individuals have the ability 
to override models, the bank should have both a policy stating the 
tolerance for overrides and a monitoring system for identifying the 
occurrence of and reasons for overrides. The performance of overrides 
should be tracked separately.
    RS-33: Banks must benchmark their risk quantification estimates 
against other sources.
    185. A bank must also assess whether it has quantified the risk 
parameters on the reference data accurately by comparing those 
estimates with alternative PD, LGD, and EAD estimates from internal and 
industry sources, a process broadly described as benchmarking. 
Benchmarking should also include the comparison of the quantification 
results derived from different risk segmentation criteria.
    186. Benchmarking allows a bank to compare the robustness of its 
estimates with those of other estimation techniques and data sources. 
Results of benchmarking exercises can be a valuable diagnostic tool in 
checking for potential weaknesses in a bank's risk quantification 
system. A bank should investigate the sources of substantial 
discrepancies between its IRB risk parameters and those observed in the 
benchmarking exercise.
Back-Testing
    RS-34: Banks must develop statistical tests to back-test their IRB 
risk quantification processes. Banks must establish tolerance limits 
for differences between expected and actual outcomes, and banks must 
have a validation policy that requires and outlines remedial actions to 
be taken when policy tolerances are exceeded.
    187. A bank must back-test its risk parameter estimates by 
regularly comparing actual segment-level default rates, loss 
severities, and exposure-at-default experience from its portfolio with 
its PD, LGD, EL, and EAD estimates. However, back-testing is only one 
element of the broader validation process, and often it will not permit 
identification of the specific reasons for discrepancies between 
expectations and outcomes. Rather, it will indicate only that further 
investigation is necessary.
    188. Random chance and many other factors will make discrepancies 
between realized outcomes and those predicted by the estimated risk 
parameters inevitable. Even for segments with a large number of 
exposures, unexpected changes in aggregate economic conditions can lead 
to differences between realized and predicted outcomes. However, if 
these discrepancies are unduly large, the bank should analyze the 
discrepancies to determine the cause. If the discrepancies demonstrate 
a systematic tendency to decrease regulatory capital, the nature and 
source of the bias requires even more detailed scrutiny.
    189. Banks have wide flexibility in developing statistical tests to 
back-test their retail risk parameter quantification and retail risk 
segmentation systems. Regardless of the back-testing method used, the 
bank should establish thresholds or accuracy tolerance levels for 
validation results. Results that breach thresholds should bring an 
appropriate response; that response should depend on the results and 
should not necessarily be to change the design of the segmentation 
system or the quantification of the risk parameter estimates. The 
bank's validation policy should describe (at least in broad terms) the 
types of required responses when relevant action thresholds are 
crossed.

IV. Data Maintenance

A. Overview

    190. Banks adopting the IRB approach for retail exposures must use 
advanced data maintenance practices to support their risk segmentation 
systems, quantification processes, validation, and control and 
oversight mechanisms described in this guidance. Timely, accurate, and 
reliable data are the foundation for retail credit risk management, and 
IRB status reinforces the importance of both data and the means to 
store, retrieve, and use them.
    191. IRB banks will implement different risk segmentation systems 
and quantification processes, and therefore their supporting data 
structure and elements will differ. Within a bank, moreover, risk 
segmentation and quantification processes may differ across business 
lines and countries. Therefore, the data structures and practices 
adopted will be unique to each bank.
    192. While banks will have substantial flexibility in the specific 
design of their data maintenance systems, the underlying principle in 
this guidance is that the data systems must be of sufficient depth, 
scope, and reliability to implement and evaluate the IRB retail credit 
risk system. The system must be able to do the following:
     Develop a risk segmentation system and assign retail 
exposures to segments;
     Develop a quantification process and assign risk parameter 
estimates to segments;
     Validate the IRB risk segmentation system criteria and 
architecture;
     Validate the IRB risk parameter estimates;
     Produce internal and public reports; and
     Support the overall retail credit risk management process.
    193. Data maintenance systems must enable banks to undertake 
necessary changes in their IRB systems and to improve methods in credit 
risk management over time. This will require that systems be capable of 
providing detailed historical data and new data elements for enhanced 
model development and new product testing.
    194. This chapter covers retail IRB data requirements and systems

[[Page 62767]]

comprising the loan characteristics specific to the bank's exposures, 
the credit characteristics of the bank's borrowers, and the performance 
history of the bank's exposures. It is expected that over time 
historical data sets used for risk segmentation and reference data for 
quantification discussed in chapters II and III will be constructed 
primarily from these internal data, but they may be supplemented by 
external data when necessary.

B. General Data Requirements

    RS-35: The bank must collect and maintain sufficient data to 
support its IRB retail credit risk system.
    195. Banks must develop data systems capable of supporting their 
risk segmentation systems and quantification processes. Given the risk 
segmentation criteria and quantification components that are necessary 
for the IRB retail credit risk system, the bank must establish 
historical databases at the individual loan level.
    196. At a minimum, the bank must maintain loan and borrower risk 
characteristics that significantly affect origination decisions (for 
example, credit score, collateral type, loan-to-value ratio), as well 
as ongoing characteristics that significantly affect account management 
decisions (for example, refreshed credit scores, utilization, payment 
history), whether or not those are used directly in the segmentation 
system.
    197. The bank must maintain data history at the loan level for all 
loans in the portfolio on performance components (for example, balance 
and payment history) and loan disposition (for example, prepayment, 
default, recoveries) necessary for PD, LGD, and EAD quantification.
    198. Data necessary to support segmentation systems and 
quantification processes may vary by business line and by country or 
wherever the key drivers of risk are unique to the portfolio, different 
data elements are available, or different measurements of loss are 
appropriate.
    199. As discussed in chapter III, banks must use the best available 
data for the development of risk segmentation systems and for 
historical reference data sets used in risk parameter quantification.
    200. Given the bank-specific basis of assigning retail exposures to 
segments, over time internal data should become the primary source of 
information for estimating IRB risk parameters. Banks using external 
data for quantification must demonstrate a strong link between (a) the 
bank's process of assigning exposures to a segment and the process used 
by the external data source and (b) the bank's internal risk profile 
and the composition of the external data.
    201. Internal data refer to data on the historical loan and risk 
characteristics and the performance of loans in a bank's own 
portfolio--even if some input components are purchased from outside 
sources. Property appraisals purchased from a third-party appraiser for 
updating LTVs of the bank's mortgage exposures would be internal data 
on loan characteristics. Credit scores purchased from a credit bureau 
for borrowers with existing exposures would be internal data on 
borrower characteristics. However, if a bank purchases extensive data 
on borrower and loan risk characteristics and the performance of other 
banks' portfolios (for example, about a new product with which the bank 
has no experience), such data would be considered external.
    202. External data may provide more accurate estimates of the risk 
parameters, particularly during the early years of IRB implementation. 
Banks should document the use of external data and retain those data in 
accordance with all of the requirements for internal data. It is 
expected that banks will improve the quality of their internal data 
over time.
    RS-36: Banks must retain all significant data elements used in the 
IRB retail credit risk system for at least five years and must include 
a period of portfolio stress. This data requirement applies to all 
loans and lines that were open at any time during this period.
    203. Banks must retain a minimum five-year loan-level history of 
the entire portfolio. The standard above establishes the minimum 
requirement for banks to retain significant data elements (key risk 
drivers) used in the risk segmentation system or in the quantification 
of the risk parameters (PD, LGD, and EAD). However, it is expected that 
banks will retain additional data elements used in their internal 
credit risk management systems.
    204. If the most recent period of portfolio stress occurred more 
than five years ago, banks must retain additional data to cover the 
stress period. These data may be in the form of representative 
statistical samples of the portfolio, rather than data from all loans. 
In addition, these data need not cover the period between the stress 
period and the most recent five-year period. The method of any sampling 
should be statistically sound and well documented.
    205. Banks must gather and retain disposition data, including 
recovery data on defaulted loans (for example, date and dollar value of 
recoveries and collection expenses) sufficient to develop LGD and EAD 
estimates. For many banks, information related to recoveries and 
collection expenses currently exists only at an aggregate level. These 
banks should develop interim solutions and a plan to improve data 
availability.
    206. Banks must retain data on losses (including recoveries, 
expenses, and dates) incurred in their revolving portfolios for at 
least five years or longer to include a period of high credit losses, 
in sufficient detail to calculate the average loss rates and the 
volatility of those loss rates over time. These parameters are 
necessary to determine eligibility for QRE capital treatment (see 
chapter III).
    207. Banks are encouraged to retain data beyond the minimum 
requirements because they will need robust historical databases 
containing key risk drivers and performance components over as long a 
historical period as possible to facilitate the development and 
validation of new, more advanced methods.
    208. A data structure designed to create a historical data 
warehouse at the loan level may take many forms. For example, the loan-
level data may be collected and stored at the business line, while 
segment-level data inputs may be stored in a centralized database. 
Ultimately, the objective is for the bank to be able to access loan-
level data, as needed, using a structure that is sufficiently robust to 
support validation and improvements in the IRB system.
Standards for Refreshed Data
    RS-37: Banks must retain refreshed data elements related to key 
credit risk drivers, performance components, and loan disposition 
consistent with advanced credit risk management standards and 
commensurate with the risk and size of the program.
    209. Maintaining up-to-date information is necessary to support a 
more risk-sensitive and accurate capital computation. This information 
may consist of refreshed information on segmentation criteria such as 
credit scores, as well as refreshed performance indicators such as 
payment history. In documenting its segmentation approach, a bank must 
specify the time frames for updating data elements involved with the 
capital calculation.
    210. For many retail products, banks update key loan and borrower 
risk characteristics and performance metrics monthly for account 
management and risk measurement purposes. For other portfolios or other 
data elements, data may be refreshed less frequently. Data

[[Page 62768]]

elements should be updated with a frequency necessary for the reliable 
measurement of credit risk for the particular portfolio or business 
line and consistent with advanced credit risk management practices.
Loan Sales
    RS-38: Banks must maintain data to allow for a thorough review of 
asset sale transactions.
    211. Asset sales may involve exposures from a variety of portfolio 
segments, and sale pricing may not be available at a granular level. It 
is important that the bank be able to quantify the impact of removing a 
portion of loans from risk segments across the portfolio and the effect 
of asset sale activity on loss mitigation strategies. Documentation for 
these transactions should be sufficient for supervisors to determine 
how asset sale activity affects the integrity of the IRB risk 
segmentation method, quantification, and the resulting capital 
calculations.
Validation and Refinement
    RS-39: Retained data must be sufficient to support IRB validation 
requirements.
    212. Data should be sufficient to facilitate the back-testing, 
benchmarking, ongoing monitoring, and developmental evidence aspects of 
the validation process described in chapters II and III.
Data Standards for Outsourced Activities
    RS-40: Banks must ensure that outsourced activities performed by 
third-party vendors are supported by sufficient data to meet IRB 
requirements.
    213. Certain processes, such as loan servicing, broker or 
correspondent origination, collection, and asset management, may be 
outsourced to or otherwise involve third parties. The necessary data 
capture and oversight of risk management standards for these portfolios 
and processes must be carried out as if they were conducted internally.
Calculating Capital Ratios and Reporting to the Public
    RS-41: At each reporting period, aggregate exposures across all 
risk segments must be reconciled to ensure that all exposures are 
accounted for appropriately.
    214. Data retained by the bank will be essential for regulatory 
risk-based capital calculations and public reporting under the Pillar 3 
disclosures. These uses underscore the need for a well-defined data 
maintenance framework and strong controls over data integrity. Total 
exposures should be tied to systems of record, and documentation should 
be maintained for this process for all reporting periods.

C. Managing Data Quality and Integrity

Documentation and Definitions
    RS-42: Banks must develop and document the process for ensuring 
data integrity and for delivering, retaining, and updating inputs to 
the IRB data warehouse. Also, banks must develop comprehensive 
definitions for the data elements used for each credit group or 
business line (a ``data dictionary'').
    215. Banks must formalize how they manage data. The full 
documentation of a bank's data management provides a means of 
evaluating whether the data maintenance framework is functioning as 
intended. Moreover, banks must be able to communicate precise 
definitions of the items to be collected. Consequently, every bank 
should develop a ``data dictionary'' to ensure consistent inputs from 
business units and data vendors and to allow third parties (such as 
auditors or bank supervisors) to evaluate data quality and integrity.
    RS-43: Banks must maintain detailed documentation on changes over 
time to the risk segmentation system and the quantification process, 
including data elements, method, and supporting processes.
    216. When changes are made to risk segmentation systems or the 
quantification processes, the bank must be able to determine how these 
changes affect capital calculations. Detailed documentation is 
necessary for the bank to identify the sources of any significant 
changes in the capital charges under IRB.
Data Access and Scalability
    RS-44: Banks must store data in a format that allows timely 
retrieval for analysis and validation of risk segmentation methods and 
parameter quantification processes. Data systems must be scalable to 
accommodate the growing needs of the business lines, the centralized 
data functions, and risk analysis over time.
    217. Banks may have a variety of storage techniques and systems to 
create their data warehouses and data marts. IRB data standards can be 
achieved by unifying existing accounting, servicing, processing, and 
workout and risk management systems, provided the linkages between 
these systems are well documented and include sufficient edit and 
integrity checks to ensure that the data can be used reliably. The data 
architecture must be designed to be scalable to allow for growth in 
portfolios, data elements, history, and product scope.
Data Gaps
    RS-45: If data gaps occur, banks must specify interim measures to 
quantify IRB risk parameters and must establish a plan to meet the data 
maintenance standards.
    218. A data gap is the absence of key data elements necessary for 
the design and application of the bank's risk segmentation system, for 
the quantification of the risk parameters, or for validation of the 
segmentation and quantification systems. One common cause of data gaps 
is a merger or acquisition. Merging or acquiring banks must develop a 
plan for creating an integrated IRB system. Data gaps may also arise as 
banks make the transition to full implementation of IRB systems.
    219. As an interim measure, banks should seek to obtain data from 
external sources to supplement internal data shortfalls. Alternatively, 
the reference data sometimes may be drawn from other sections of the 
portfolio, but only when the business lines and loan and borrower 
characteristics are sufficiently similar. The bank must document any 
transitional steps and should take an appropriately conservative 
approach to quantification when data gaps exist.
    220. The level of effort placed on filling data gaps should be 
commensurate with the current and anticipated volume of exposures to be 
incorporated into the bank's IRB system.

V. Control and Oversight Mechanisms

A. Overview

    221. Risk management processes and controls, which are the 
foundation of retail lending activities, are essential to product 
development, pricing, underwriting, account management activities, 
portfolio performance forecasting, and economic capital modeling and 
long-term capital planning. Banks will use similar processes and 
controls to ensure the accuracy of their segmentation, quantification, 
and regulatory capital levels.
    RS-46: IRB banks must implement an effective system of controls and 
oversight.
    222. This system must include controls over lending activities, 
independent review, transparency, accountability, use of risk parameter 
estimates for internal risk management purposes, internal and external 
audit, and board and senior management oversight. Banks will have 
flexibility in

[[Page 62769]]

how these elements are combined, provided they incorporate sufficient 
checks and balances to ensure that the credit risk management system is 
functioning properly.
    223. IRB banks must have controls and oversight to ensure the 
integrity of the risk segmentation system and the accuracy of the risk 
parameter estimates used for determining regulatory capital under the 
IRB framework. Table 5.1 lists the key components of an IRB control and 
oversight system. These controls can be combined or structured to 
reinforce one another in a variety of different ways.

              Table 5.1.--Control and Oversight Mechanisms
------------------------------------------------------------------------
 
------------------------------------------------------------------------
Controls over retail lending        A structure and system of management
 activities.                         and controls must be established to
                                     ensure credit quality and data
                                     integrity.
Accountability....................  Responsibilities and lines of
                                     authority should be documented in
                                     bank policy.
Independent review................  An independent review process must
                                     evaluate the integrity of the IRB
                                     risk segmentation system and
                                     quantification process.
Transparency......................  The IRB retail credit risk system
                                     must be sufficiently transparent to
                                     enable third parties to understand
                                     key aspects of the segmentation
                                     system and quantification process.
Use of risk estimates.............  IRB risk parameter estimates must be
                                     consistent with internal risk
                                     measurements that are used to guide
                                     risk management activities and
                                     financial management.
Internal and external audit.......  Internal and external audit must
                                     assess the effectiveness of control
                                     and oversight mechanisms and
                                     overall compliance with the IRB
                                     standards.
Board and senior management         Ultimate responsibility for the
 oversight.                          performance of the IRB retail
                                     credit risk system rests with
                                     senior management and the board.
------------------------------------------------------------------------

B. Controls Over Lending Activities

    RS-47: Banks must have an independent risk management function that 
provides oversight of retail lending activities.
    224. An independent risk management function is not directly 
involved in the credit decision process. The group's staff members 
should be compensated principally on how effectively they manage credit 
risk. The risk management function should be responsible for setting 
credit policies and ensuring that credit standards are followed. Retail 
credit review and compliance management are functions that should 
augment and support risk management activities.
    RS-48: Banks must have an effective loan review function for retail 
credit portfolios.
    225. An effective loan review for retail credit is an essential 
control for all IRB banks. Loan review must be independent of the 
lending process. The numbers, experience, and knowledge of personnel in 
loan review should be commensurate with the complexity and risk of the 
bank's retail loan portfolios.
    226. The scope of reviews should provide an assessment of the 
quality of risk management and quantity of risk in retail loan 
portfolios. The frequency of reviews should be based on the risk and 
size of the portfolios. Reports should clearly identify any concerns. 
Banks should have a process for timely resolution of issues and 
weaknesses identified by loan review.
    RS-49: A quality control function must confirm that all retail 
lending activities follow established policies.
    227. The purpose of quality control is to provide ongoing assurance 
that all retail lending activities adhere to the bank's policies and 
procedures. The quality control program should monitor and evaluate the 
integrity of credit origination, account management, and collection 
activities and should provide timely feedback to senior management. 
Without strong quality control systems governing all aspects of the 
lending process, the IRB retail credit risk system can be significantly 
compromised.
    228. The quality control function should be formally established 
and operate independently of the loan production process, collections, 
and servicing functions. The quality control program should have 
established operating procedures and stated requirements for sample 
size and selection. Coverage of this function should include 
statistically valid samples.
    229. The quality control function should generate monthly reports 
to appropriate levels of management, outlining findings and identifying 
policy exceptions. This information should be used to address 
weaknesses in lending activities. The function should seek corrective 
action as necessary.
    RS-50: Management information systems (MIS) must be sufficiently 
comprehensive to monitor and measure credit quality and performance and 
to allow proactive and effective risk management.
    230. Comprehensive MIS is needed to support risk management. 
Reports should measure risk for each stage of the life-cycle for retail 
loans and provide early warning of changes in risk profiles. Front-end 
reporting generally includes score distribution, score overrides, 
exception reporting, and other pertinent borrower and collateral 
information. Ongoing portfolio MIS should provide information about the 
overall risk profile, portfolio performance, and the direction of risk, 
including score distributions, changes in score distributions, early 
default analysis, and vintage analysis. Collection reporting should 
include delinquency roll rates, static pool cash collection analysis, 
and data on volumes and performance for workouts and loss mitigation 
programs. Banks must have a process to ensure that reports are accurate 
and consistent.
    RS-51: Adequate controls and monitoring systems must be in place to 
effectively supervise all third parties involved in the lending 
process.
    231. Vendor management should include a process to identify, 
monitor, manage, and control the risks posed by third-party providers. 
Vendor arrangements should be established based on adequate due 
diligence and should include written contracts that outline duties, 
obligations, and responsibilities of both parties. Banks are expected 
to provide ongoing oversight for third-party arrangements to ensure 
that activities are conducted in a safe and sound manner and in 
compliance with the law. Underlying controls should be the same as if 
the bank were conducting the activity directly.
    232. Banks frequently use third parties such as brokers, dealers, 
and correspondents in the loan origination process. While these sources 
of new loans provide positive benefits, they also warrant strong 
oversight. For loans that involve brokers and dealers, banks should 
ensure that adequate controls, such as loan verification activities, 
credit scoring, and the collateral valuation process, exist over loan

[[Page 62770]]

processing. Strong control processes over brokers and dealers can help 
ensure that underwriting decisions are based on reliable information. 
For correspondent originations, banks should have adequate monitoring 
systems in place to ensure that loans meet the bank's internal 
underwriting requirements.

C. Accountability

    RS-52: Bank policies must identify individuals responsible for all 
aspects of the retail IRB credit risk system.
    233. Responsibilities and lines of authority should be documented 
in bank policy. Personnel should have the tools and resources necessary 
to carry out their responsibilities, and their performance should be 
evaluated against clear and specific objectives. Individuals should be 
held accountable for complying with applicable policies and ensuring 
that those aspects of the IRB system that are within their control are 
unbiased and accurate.

D. Independent Review of Retail IRB Processes

    RS-53: Banks must have a comprehensive, independent review process 
that is responsible for ensuring the integrity of the IRB risk 
segmentation system and quantification process.
    234. The review process should be independent of the individuals 
who develop the underlying segmentation systems and perform 
quantification activities. The activities of this review process could 
be distributed across multiple areas or housed within one unit. 
Organizations will choose a structure that fits their management and 
oversight framework. For example, the independent review might be 
conducted by loan review or other similar units, subject to the 
independence requirement above. Individuals performing the reviews 
should possess the requisite technical skills and expertise.
    235. The review should be conducted at least annually and should 
encompass all aspects of the process, including:
     Compliance with policies and procedures;
     Design and effectiveness of the segmentation system;
     Quantification process and accuracy of parameter 
estimates;
     Model development, use, and validation;
     Adequacy of data systems and controls; and
     Adequacy of staff skills and experience.
    236. The review process should identify any weaknesses, make 
recommendations, and ensure corrective action. Significant findings of 
IRB reviews must be reported to senior management and the board.

E. Transparency

    RS-54: IRB banks must have a transparent retail IRB process.
    237. Transparency is the ability of third parties, such as loan 
reviewers, auditors, and supervisors, to understand the design, 
operations, and accuracy of the risk segmentation system and 
quantification process for retail IRB.
    238. Transparency in the risk segmentation system and 
quantification process may be achieved through documentation that 
covers the following:
     The segmentation design, including selection of risk 
drivers, use of refreshed information, and granularity of segmentation;
     Parameter estimates and the processes used for their 
estimation, including significant adjustments and assumptions;
     Data requirements;
     Documentation for model development, implementation, and 
validation; and
     Specific responsibilities of and performance standards for 
individuals and units involved in the retail IRB process and its 
oversight.

F. Use of Risk Estimates

    RS-55: Retail IRB risk parameter estimates must be consistent with 
risk estimates used to guide day-to-day retail risk management 
activities.
    239. Banks must demonstrate that IRB segmentation and IRB risk 
parameter estimates are consistent with those used by bank management 
in its planning, execution, and oversight of retail lending activities. 
Risk drivers for IRB segmentation purposes should correspond to risk 
drivers used as part of the overall risk management of the lines of 
business. IRB risk parameter estimates of PD, LGD, and EAD should be 
incorporated in credit risk management, internal capital allocation, 
and corporate governance. Banks should compare actual default rates 
with PD and actual dollar loss rates with internal forecasts for each 
of the retail IRB products.

G. Internal and External Audit

    RS-56: Internal and external audit must annually evaluate 
compliance with the retail IRB capital regulations and supervisory 
guidance.
    240. Internal audit should report to the board and management on 
the bank's compliance with the retail IRB standards, including ones 
applicable to the segmentation system and estimation of the IRB risk 
parameters. This report will allow the board and management to affirm 
that the risk segmentation system, the quantification process, and the 
surrounding controls are in compliance with IRB standards. This will be 
critical for public disclosure and ongoing review by supervisors. As 
part of its review of control mechanisms, internal audit should 
evaluate the depth, scope, and quality of the independent review and 
quality control functions.
    241. As part of the process of certifying financial statements, 
external auditors should, to the extent appropriate under applicable 
auditing and professional standards, ascertain whether the IRB system 
is measuring credit risk appropriately and confirm that the bank's 
regulatory capital position is fairly presented. Auditors should also 
evaluate, to the extent appropriate under these standards, the bank's 
internal control functions relating to regulatory capital and its 
compliance with the risk-based capital regulation and supervisory 
guidance.

H. Corporate Oversight

    RS-57: The full board or a designated committee of the board must 
review and approve key elements of the IRB system.
    RS-58: Senior management must ensure that all components of the IRB 
system, including controls, are functioning as intended and comply with 
the risk-based capital regulation and supervisory guidance.
    242. Senior management's oversight is expected to be more active 
than that of the board of directors. Senior management must have an 
extensive understanding of credit policies, underwriting standards, and 
account management activities (including collections) and must 
understand how these factors affect the IRB risk segmentation system, 
risk-parameter estimates, and data maintenance requirements.
    243. The depth and frequency of information provided to the board 
and senior management must be commensurate with their oversight 
responsibilities and the condition of the bank. The board should be 
provided with periodic high-level reports summarizing the performance 
of the retail IRB credit risk system. Senior management should receive 
more detailed reports covering topics such as:
     Risk profile by retail portfolio;
     Actual losses by risk segment compared with the IRB risk 
parameter estimates (PD, LGD, and EAD), with emphasis on unexpected 
results;
     Changing portfolio trends and risks;

[[Page 62771]]

     Reports measuring changes in regulatory and economic 
capital;
     Reports generated by the independent review function, 
quality control, audit, and other control units; and
     Results of capital stress testing.
    244. Although all of a bank's controls must function smoothly, 
independently, and in concert with the others, the direction and 
oversight provided by the board and senior management is critical to 
ensuring that the IRB system is functioning properly.
    245. For retail portfolios that are managed across legal entities, 
the board of directors and senior management of each insured depository 
institution must have sufficient information about its exposures to 
accurately assess and report on its own risk.
    246. Senior management should confirm that activities conducted 
across multiple legal entities meet the following conditions:
     Products are managed centrally using consistent policies;
     Segments that cross multiple legal entities meet the 
requirements of chapter II to ensure that they have homogeneous risk 
characteristics;
     Exposures outside the United States are not grouped with 
domestic exposures; and
     Validation and back-testing activities include the 
additional step of ensuring that minimum capital requirements for each 
entity are accurate.

Appendix A: Process Analysis Examples

Example 1: A Seamless Application of the Four Stages of 
Quantification (See Paragraph 70)

    Consider a bank that has been making indirect installment loans 
through furniture stores for a number of years. Seven years of 
internal data history are available, over a period including a 
significant recession. The bank has segmented this portfolio over 
the whole period in a consistent manner: by bureau score, internal 
behavioral score, and monthly disposable income. In addition, LGDs 
for this portfolio have demonstrated significant cyclical 
variability over the period covered by the bank's data history.
    The bank can empirically show that the participating furniture 
retailers, underwriting criteria, and collection practices have 
remained reasonably stable over the seven-year period, and the 
definition of default has been consistent with the IRB definition. 
However, there are frequent changes in the bank's products and in 
the borrowing population that affect the risk characteristics of its 
loans, so the bank uses only the most recent seven-year data history 
as new data become available (assuming that the data includes a 
period of recession).
    The PD is calculated as the average of the seven annual PDs. The 
LGD is the loss severity observed during periods when credit losses 
for this type of product have been high. The EAD for non-defaulted 
loans is calculated as the outstanding loan amount at the time of 
capital measurement plus any accrued but unpaid interest and fees.
    In this example, the four stages have not been explicitly 
mentioned or applied. Nonetheless, at the level of detail presented 
(which is clearly somewhat simplified), the quantification appears 
to satisfy most of the standards in the chapter (subject, of course, 
to validation).
    If the bank desires, it can put its quantification into the 
following four-stage framework:
    a. The bank's own historical data serve as the reference data;
    b. Estimation consists of calculating the historical average PD, 
the recessionary LGD, and the outstanding balance by segment;
    c. Mapping consists primarily of ensuring that the segmentation 
schemes and the definition of default are consistent between the 
reference data portfolios and the bank's existing portfolios; and
    d. Application is a matter of using the risk parameter estimates 
from the reference portfolios for each segment of the existing 
portfolios in the regulatory capital formulas.
    Thus, as discussed in the main chapter text, the four stages of 
quantification are not intended as a set of rigid requirements that 
must be followed in every detail in all circumstances. Rather, they 
should be seen as a conceptual framework, and as an analytical and 
implementation guide for those institutions whose data histories, 
institutional circumstances, or unusual complexities require the 
greater detail and specificity.

Example 2: Quantification of the PD for First-Lien Mortgages (See 
Paragraph 106)

    a. For the past four years a mortgage portfolio has been 
concentrated in a less risky geographic region than the historical 
portfolio, whose data history goes back several more years. The bank 
analyzes external mortgage data by geographic region over the same 
time period to identify regional differences in default rates. 
Analysis of the reference data indicates similar regional 
differences.
    b. The recent four-year period does not include a period of 
stress, so the bank uses its full internal data history to encompass 
a period of stress. To estimate the PD parameter over a long run of 
data history that is also comparable to the current portfolio, the 
bank develops a statistical model of the PD over combined internal 
and external performance history. The variables used as PD 
predictors included geographic region, loan and borrower risk 
characteristics, loan-to-value ratios, and lagged mortgage 
foreclosure rates by region. With this model the bank claims that it 
is able to fully utilize its 13-year history of internal data as 
well as take into account the effects of the more recent geographic 
change in its portfolio.
    Process Analysis for Example 2:
    Data--The existing portfolio of first-lien mortgages is 
segmented by LTV, credit score, tenor, fixed-rate vs. ARM, and debt-
to-income ratio. For a given segment, the bank has good historical 
data from its own portfolio. The reference data consist of nine 
years of lifetime internal performance history for loans originated 
between 1990 and 1999, which are concentrated within the riskier 
geographic region, plus four years of recent internal history (2000-
2003). The internal data is supplemented by external regional 
mortgage data over the full 13-year history (1999-2003).
    Estimation--The bank builds a statistical model that estimates 
PD as a function of regional foreclosure rates for the previous two 
quarters, the loan-to-value ratio, credit score, debt-to-income 
ratio, loan tenor, and geographic region, and it builds separate 
models by product type (e.g., fixed-rate vs. ARM). A similar model 
of LGD is estimated using a regression model that incorporates 
economic factors. An LGD estimate reflective of periods of high 
credit losses in the mortgage market is produced by stressing the 
economic factors in the model. The model results are robust in terms 
of the standard statistical diagnostic tests. The model has 
continued to perform satisfactorily in validations outside the 
development sample.
    Mapping--Since the 1990-1999 period, the bank has shifted much 
of its first-lien mortgage business to a different region of the 
country, one that historically has experienced lower default rates. 
The bank segments its portfolio by region and borrower and loan 
characteristics utilized in the model to produce a long-run average 
PD estimate by region, so as to take the lower regional default 
rates into account. An ``economic downturn'' LGD is also calculated 
by the same segmentation. Therefore, in mapping from the reference 
data to its existing portfolio data the bank assigns the average PD 
and the economic downturn LGD per segment of exposures in the 
existing portfolio, as estimated by the models.
    Application--The bank will now apply the regression models to 
its existing portfolio to estimate the PD and LGD values for each 
segment in the first-lien mortgage portfolio. It will measure EAD 
for non-defaulted loans as the present outstanding balance per 
segment plus any accrued but unpaid interest and fees. Then it will 
enter the three risk parameters into the IRB mortgage formula to 
assess the minimum required regulatory capital for each segment.

Example 3: PD Estimation From Dollars Defaulted and Present 
Portfolio Value (See Paragraph 108)

    Paragraph 101 defines PD in terms of accounts, not dollars: the 
number of defaulted accounts during the course of a year divided by 
the number of accounts open at the beginning of the year. This 
example discusses issues involved with methods that attempt to 
derive PD from dollar loss rates. If a bank chooses to derive a PD 
in this manner, the bank will need to consider a variety of factors 
to ensure that the PD estimate is an accurate reflection of the 
expected rate of defaults on an account basis.
    a. A credit card bank directly measures its average dollars of 
economic losses for each segment and uses the percentage of dollars 
defaulted, rather than as the percentage of loans defaulted, as the 
estimate of PD.

[[Page 62772]]

Specifically, the ratio employed is the gross loss divided by the 
exposure at default. The gross loss (before recovery) is directly 
measured on a segment of accounts over a one-year time horizon. The 
bank estimates exposure at default (EAD) for a segment as the 
current outstanding balances plus the expected drawdowns on open 
balances if all accounts default (including accrued but unpaid 
interest and fees at the time of default).
    b. The bank's risk segmentation system separates exposures by 
size of credit line and credit line utilization as well as by credit 
score. If the segmentation appropriately controls for current 
balances and credit lines, then it should produce accurate estimates 
of both PD and EAD. The bank regularly validates the accuracy of the 
EAD estimates and the consistency of the percentage-of-dollars-
defaulted measure with the account default rate.
    Process Analysis for Example 3:
    Data--The historical reference data consist of measurements of 
the outstanding dollar balances and open credit lines at the 
beginning of the year. For accounts that defaulted over the 
following year the gross defaulted balances are also measured. The 
aggregate dollar amounts are measured for each segment.
    Estimation--The bank's dollar PD parameter is estimated as the 
long-run average of the one-year PDs. Each one-year PD is measured 
as the gross balances of defaulted loans divided by the estimated 
EAD. The following example illustrates why granular segmentation by 
balance and credit line can be important. In the first row of the 
following table, all loans with account PD equal to 1% are grouped 
together in a single segment. Using an estimatedLEQ of 0.7 derived 
from historical reference data, the Gross Loss / ED measure equal 1% 
and is equivalent to the account PD. In the second row of the table 
however, although all loans with account PD equal to 1% are still 
included in the segment, the Gross Loss/EAD measure has fallen to 
0.94% and is therefore no longer an acceptable proxy for the account 
PD.

--------------------------------------------------------------------------------------------------------------------------------------------------------
                                       Average      Average                                            Estimated
                                       balance       credit       Number       Total        Total       percent     Estimated                Gross  loss/
            Account  PD                  per        line per   accounts in  outstanding    undrawn      drawdown       EAD      Gross  loss      EAD
                                       account      account      segment      balance       lines        (LEQ)
--------------------------------------------------------------------------------------------------------------------------------------------------------
1.0%...............................         $225         $600        2,000     $450,000     $750,000          70%     $975,000       $9,750         1.0%
1.0%...............................         $285         $760        2,000     $570,000     $950,000          70%   $1,235,000      $11,550        0.94%
--------------------------------------------------------------------------------------------------------------------------------------------------------

    The reason for this discrepancy can be found in the granularity 
of the bank's segmentation process. By grouping together all loans 
with account PD equal to 1%, the bank is combining loans with 
significantly different average balances per account and average 
credit lines. They are also using an estimate for LEQ (0.7) based on 
historical data for particular portfolios of loans with PD equal to 
1% that is not accurate for portfolios with different distributions 
of loans by outstanding balances and credit lines.
    This can be seen by looking at a finer segmentation of the 
portfolios. In the table below, the segment from the top row in the 
previous table is divided more finely, by average balance and credit 
line. The historically estimated LEQs differ significantly between 
the segments, and the 0.7 LEQ in the previous table represents a 
weighted average of the two different segment values. Because the 
LEQ estimate is the weighted average of the two segment LEQs, then 
as long as the distribution of accounts between the two segments 
remains steady the Gross Loss/EAD measure shown in the first table 
equals 1% and is equivalent to the account PD.

--------------------------------------------------------------------------------------------------------------------------------------------------------
                                       Average      Average                                            Estimated
                                       balance       credit       Number       Total        Total       percent     Estimated                Gross  loss/
            Account  PD                  per        line per   accounts in  outstanding    undrawn      drawdown       EAD      Gross  loss      EAD
                                       account      account      segment      balance       lines        (LEQ)
--------------------------------------------------------------------------------------------------------------------------------------------------------
1.0%...............................         $150         $400        1,000     $150,000     $250,000          90%     $375,000       $3,750         1.0%
1.0%...............................         $300         $800        1,000     $300,000     $500,000          60%     $600,000       $6,000         1.0%
------------------------------------
                                      Weighted
Aggregated 1% PD Segment              Average LEQ
-----------------------------------------------------------------------------------------------------
1.0%...............................         $225         $600        2,000     $450,000     $750,000          70%     $975,000       $9,750          1.0
--------------------------------------------------------------------------------------------------------------------------------------------------------

    In the next table, the larger segment (from the second row in 
the first table above) is divided into two finer segments in the 
same manner as previously. In fact, the average balances, average 
lines, and LEQs are all the same as in the previous case. The only 
change is in the proportion of accounts in each segment. However, by 
using the LEQ of 0.7 derived from the coarser segmentation, the bank 
estimated Gross Loss/EAD as 0.94 in the second row of the first 
table. The finer, more accurate, weighted LEQ of 0.62 produces a 
Gross Loss/EAD measure of 1.0%, equivalent to the account PD.

                                                       Segmentation by PD, Balance and Credit Line
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                                                       Estimated
                                       Average      Average       Number       Total        Total       percent     Estimated                Gross  loss/
             Account PD              balance per  credit line  accounts in  outstanding    undrawn      drawdown       EAD      Gross  loss       EAD
                                       account    per account    segment      balance       lines        (LEQ)
--------------------------------------------------------------------------------------------------------------------------------------------------------
1.0%...............................         $150         $400          200      $30,000      $50,000          90%      $75,000         $750         1.0%
1.0%...............................         $300         $800        1,800     $540,000     $900,000          60%   $1,080,000      $10,800         1.0%
------------------------------------
                                      Weighted
Aggregated 1% PD Segment              Average LEQ
-----------------------------------------------------------------------------------------------------
1.0%...............................         $285         $760        2,000     $570,000     $950,000          62%   $1,155,000      $11,500         1.0%
--------------------------------------------------------------------------------------------------------------------------------------------------------


[[Page 62773]]

    Thus we see that, with the proper segmentation criteria and 
sufficiently granular segmentation, the Gross Dollar Loss/EAD 
measure can produce a PD that is equivalent to the correct account 
PD. If a bank were to use the coarser segmentation shown in the 
first table (i.e., all accounts with account PD=1), the bank would 
have to carefully monitor the changes in distribution of accounts 
within this broader segment and update the weighted average LEQ on a 
timely basis. Given how rapidly portfolio composition can change in 
credit card markets, this may be a challenging task.

    Note: Another method of calculating the PD from dollar 
measurements used at some institutions is to estimate the PD for a 
segment as the accumulated gross losses at the end of a one-year 
period divided by the outstanding balances at the beginning of the 
year. This does not provide an estimate equivalent to an account 
default rate if initial balances on accounts that eventually default 
are significantly different from those that do not default, which is 
generally the case. Consider the examples in the following table. 
(For simplicity, these examples assume there is no amortization of 
principal over the year.)


----------------------------------------------------------------------------------------------------------------
                                                             Average       Average
                   Number                       Total       beginning     beginning                 Gross Losses/
Number  total    defaulted     Account  PD    beginning   balance  non-    balance    Total  gross     beginning
   accounts       accounts                   outstanding    defaulted     defaulted       losses     outstanding
                                              balances      accounts      accounts                    balances
----------------------------------------------------------------------------------------------------------------
        1000             20           2.0%    $1,000,000        $1.005          $750       $15,000          1.5%
        1000             20           2.0%    $1,000,000          $995        $1,250       $25,000          2.5%
----------------------------------------------------------------------------------------------------------------

    As shown in the table, if balances on accounts that default are 
higher than balances on those that do not (which is the more common 
situation), then the Gross Losses/Outstanding Balances measure will 
overestimate PD. Conversely, if defaulted accounts have lower 
balances, the Gross Loss/Outstanding Balances measure will 
underestimate PD.
    Mapping--To develop a risk segmentation system that produces 
homogeneous and stable segments, the bank identifies the drivers of 
both default risk and drawdowns and then segments by these drivers. 
The mapping would involve linking segments in the reference data to 
segments in the present portfolio using the same risk segmentation 
system. However, during recessionary periods, the bank monitors 
changes in the market and economic environment that could change the 
relationships between default risk and drawdowns and the underlying 
drivers of these risks. If there were systematic changes, then the 
risk segmentation system would need to be updated.
    Application--The application is generally a straightforward, 
direct application of estimates from segments in the reference data 
to segments in the existing portfolio. Estimates would be adjusted 
if the default risk were expected to change systematically from 
previous periods, for example, because of a trend toward higher 
credit lines.

Example 4: PD Quantification With Adjustments for Seasoning (See 
Paragraphs 109-112)

    a. PDs for a bank's credit card portfolio exhibit a 
characteristic time profile by age--a seasoning curve. As a result 
of the bank's analyses, the shape of this seasoning curve has been 
established by specific products and borrower credit quality at 
origination utilizing data from vintages over the last five years. 
The bank regularly analyzes new vintages to capture changes in the 
characteristic time profile of PDs over changing economic and market 
environments. Systematic changes are incorporated into new seasoning 
curves.
    b. The risk segmentation system criteria for seasoned and 
unseasoned loans include updated account age, or ``time on books.''
    c. For unseasoned loans, if seasoning effects are material, the 
PD is estimated as an annualized cumulative default rate over the 
remaining expected life of the loans. For seasoned loans the PD 
should simply be measured as a long-run average of the one-year-
ahead PDs.
    Process Analysis for Example 4:
    Data--The main reference data consists of five years (or more) 
of portfolio history. Segments are defined by updated borrower, 
product, and loan characteristics including account age. 
Supplemental reference data consist of vintage analyses of similar 
products originated within the same time period, providing seasoning 
curves specific to borrower credit quality at origination, product, 
and loan type. Given the level of the annualized default rate 
observed in the early history of a cohort, the historical seasoning 
curves should indicate the trend that PDs follow over the remaining 
expected life of the loans.
    The bank presents analyses indicating that the seasoning curve 
can be reasonably specified by borrower credit quality at 
origination and carefully monitors new cohorts for any deviation of 
the time profile of one-year PDs from the corresponding seasoning 
curve.
    Estimation--For seasoned loans, a long-run average PD is 
calculated for each segment by updated borrower, product, and loan 
characteristics, including loan age. For unseasoned loans, the PD is 
the estimated annualized cumulative default rate over the remaining 
expected life of the loans.
    Mapping--The risk segmentation system of the present portfolio 
is the same as that employed for the reference data. This makes the 
mapping straightforward along the lines of refreshed borrower credit 
quality. However, the bank should ensure while mapping that the 
product characteristics in the reference data are mapped to 
equivalent product characteristics in the present portfolio.
    Application--At the application stage, the long-run PD estimated 
from the reference data may simply be applied to the matching 
segments in the existing portfolio.

Appendix B: Technical Examples

Example 1 From General Standards (See Paragraph 91 and Standard RS-
13)

    The following example illustrates one possible solution when 
sufficient internal historical data is not available for an entire 
portfolio. The bank may be able to identify sub-samples within its 
portfolio that experienced increased default rates during the 
available length of history, even though the aggregate portfolio may 
not have realized such a trend. For example, data may be available 
from local or regional recessions in New England (late 1980s and 
1990-1995), Texas (1983-1989), or California (1991-1995). The bank 
must be able to demonstrate that the drivers of high default rates 
in these regional recessions can be extrapolated to the entire 
portfolio as well as justify and document any resulting adjustments 
that would be necessary in the mapping and application stages.

Example 2 From General Standards (See Paragraphs 93 and 130 and 
Standard RS-14)

    At least two common types of mapping challenges may arise in 
regard to PD, LGD, and/or EAD quantification:
    a. First, even if similarly named characteristics are available 
in the reference data and portfolio data, they may not be directly 
comparable. For example, in a portfolio of auto loans, the 
particular types of auto loans (for example, new or used, direct or 
indirect) may vary from one application to another. Hence, a bank 
should ensure that linked characteristics are truly similar. 
Although adjustments to enhance comparability can be appropriate, 
they must be rigorously developed and documented.
    b. Second, levels of aggregation may vary. For example, the 
reference data may only broadly identify collateral types--say, 
broad categories of automakers. The bank's information systems for 
its portfolio might supply more detail such as auto makes and models 
plus the age and condition of vehicles. To apply the estimates 
derived from the reference data, the bank may regroup the existing 
portfolio in order to match broader aggregations in the reference 
data.

Example 3 From the PD Estimation Standards (See Paragraph 107)

    The following examples illustrate possible PD estimation methods 
that might appear in bank practice and potential problems with some 
methods:

[[Page 62774]]

Example 3a: Adjustments When PDs Are Measured Over a Shorter Time 
Horizon and Then Annualized

    In practice the account default rate may be estimated at a 
monthly or quarterly rate and ``annualized'' to produce the 
equivalent yearly default rate. However, this annualized rate may 
not be accurate over a one-year horizon if the bank does not track 
loans that migrate within the year. For example, consider a segment 
with very high credit quality--call it the ``superprime'' segment. 
Over the year, many accounts that default have first migrated to 
lower credit quality segments at stages during the year. So, 
annualizing the quarterly default rate for the ``superprime'' 
portfolio would be an underestimate of the true one-year default 
rate. The PD should be measured from actual portfolio performance of 
all loans in the bucket over a full one-year horizon.
    The following example presents this issue. The quarterly 
transition rates between the three non-default rating classes 
(``superprime,'' ``prime,'' and ``subprime'') and the transition 
rates into default are listed below:

----------------------------------------------------------------------------------------------------------------
                                                                       Beginning of quarter
                                                 ---------------------------------------------------------------
                                                    Superprime         Prime         Subprime         Default
----------------------------------------------------------------------------------------------------------------
End of Quarter:
    Superprime..................................             94%              2%              1%               0
    Prime.......................................              5%             94%              3%               0
    Subprime....................................              1%              3%             95%               0
    Default.....................................            0.1%              1%              2%            100%
----------------------------------------------------------------------------------------------------------------

    A particular segment is 100% superprime at the beginning of a 
one-year time horizon. Over each quarter some accounts migrate into 
lower quality states with correspondingly higher default rates. As a 
result of this migration, the population distribution among the 
rating classes changes over each quarter. The Superprime, Prime, and 
Subprime columns of the following table show the changing 
distribution for these loans that were all superprime as of January 
1. For example, at the end of the second quarter, only 88% of the 
surviving loans remain superprime, 9% are now prime, and 2% are 
subprime.
    The last column represents the cumulative default rate for these 
formerly Superprime loans. That is, at the end of the second quarter 
0.26% will have defaulted; at the end of the third quarter, 0.49% 
will have defaulted, and at the end of the year, a total of 0.77% of 
the original all-Superprime segment will have defaulted, which is 
substantially higher than four times the quarterly default rate, or 
0.4%.\11\
---------------------------------------------------------------------------

    \11\ The cumulative default rate is the sum of the defaults at 
the end of the previous period plus new defaults during the period 
just ended. The new defaults are determined as the sum of the 
proportions of loans in each rating category times the respective 
default rate for that category. For example, at the end of the 
second quarter, the new defaults equal the 94% of the loans that 
were still Superprime at the beginning of the period times the 
Superprime default rate of 0.1% plus the 5% of loans that had become 
Prime times the Prime default rate of 1%; plus the 1% of loans that 
had become Subprime times the Subprime default rate of 2%. This 
yields a default rate during the second quarter of 0.25%, which is 
added to the 0.1% default rate from the end of the first quarter to 
produce a cumulative rate of 0.26% at the end of the second quarter.

----------------------------------------------------------------------------------------------------------------
                                                    Superprime         Prime         Subprime         Default
                      Time                           (percent)       (percent)       (percent)       (percent)
----------------------------------------------------------------------------------------------------------------
January 1.......................................             100               0               0               0
End of Quarter 1................................              94               5               1            0.10
End of Quarter 2................................              88               9               2            0.26
End of Quarter 3................................              83              13               3            0.49
                                                                                                 ---------------
End of Quarter 4................................              78              17               4            0.77
----------------------------------------------------------------------------------------------------------------

    Note that this illustration assumes that the transitions from 
one quarter to the next are the same for each quarter throughout the 
year. In practice, they may vary from quarter to quarter for many 
reasons.

Example 3b: Portfolio Growth and the Timing of Default Measurements

    The method and timing of the measurement of portfolio growth and 
defaulted accounts for a pool can also bias the PD estimates. 
Defaulted accounts would be measured at year-end and should not 
include accounts opened within the year. The total number of 
accounts should be measured at the beginning of the year. When the 
total number of accounts is measured concurrently with the number of 
defaulted accounts, if the total pool size increases (decreases) 
substantially over the one-year observation period, the PD could be 
underestimated (overestimated) substantially.
    In the following example, the portfolio shows four alternative 
growth rates over one year: (1) The portfolio shrinks by 5 percent, 
(2) the portfolio shrinks by 10 percent, (3) the portfolio grows by 
5 percent, or (4) the portfolio grows by 10 percent:
    The portfolio starts at the beginning of the year with 1 million 
accounts and $100 million in outstanding balances, or an average of 
$100 per account. For simplicity it is assumed that the PD and 
average account balance remain constant over the year while the 
number of accounts changes.

----------------------------------------------------------------------------------------------------------------
                                     Total portfolio accounts        Accounts        PD front     PD from end of
  Annual portfolio growth rate   --------------------------------  defaulted  by   start of year  year portfolio
                                   Start of year    End of year    end  of year      portfolio       (percent)
----------------------------------------------------------------------------------------------------------------
-5%.............................       1,000,000         950,000          20,000             2.0             2.1
-10%............................       1,000,000         900,000          20,000             2.0             2.2
5%..............................       1,000,000       1,050,000          20,000             2.0             1.9
10%.............................       1,000,000       1,100,000          20,000             2.0             1.8
----------------------------------------------------------------------------------------------------------------
Note: It is assumed that all 20,000 defaults that occurred during the year were accounts that were part of the
  portfolio on January 1. The Other Retail risk weight curve was used for this example, and LGD is assumed to be
  90% in all four cases.


[[Page 62775]]

    This example shows clearly how the use of the end-of-year 
portfolio size, rather than the number of accounts that were open at 
the beginning of the year, produces significant misestimation of PD, 
which should equal 2.0% in all four cases.

Example 4 From the PD Estimation Standards (See Paragraph 102)

    A bank uses the last five years of internal default history to 
estimate a long-run average PD for each pool of retail exposures. 
However, it recognizes that the internal experience does not include 
any years of portfolio stress. To remedy this and still take 
advantage of its experience, the bank uses external loss data to 
adjust the PD estimates upward in the years of economic downturn or 
systematic economic stress. (An example of an external data source 
would be historical mortgage default data purchased from a vendor.). 
Using the external data, the bank creates an index by calculating 
the ratio between each year's mortgage default rate per pool and the 
long-run average rate per pool of exposures over the last five 
years, both from the external data. The bank assumes that the 
relationship observed in the external data applies to its own 
mortgage portfolio, and it uses the index to adjust the estimates 
for the internal data accordingly. If the bank rigorously validates, 
justifies, and documents these adjustments, it would satisfy the 
standard.

Example 5 From the LGD Estimation Standards (See Paragraphs 127-
129)

    A bank determines that a business unit forms a homogeneous pool 
for the purposes of estimating loss severity. That is, although the 
loans in this pool may differ in some respects, the bank determines 
that they share a similar loss experience in default. The bank must 
provide reasonable support for its claim through an analysis of 
lending practices and available internal data. If it does so 
convincingly, a common pool across a business unit is consistent 
with the standard.

Example 6 From the LGD Estimation Section (See Paragraphs 127-129)

    A bank divides observed defaults in the reference pool according 
to geographic region and loan-to-value in a mortgage portfolio. One 
of the pools has too few observations to produce a reliable 
estimate. By augmenting the loss data in this pool with data from 
other pools (for example, neighboring geographic regions with the 
same LTV), the bank calculates an estimate of the severity. The bank 
must validate, justify, and document the accuracy of this proxy 
value.
    In another example, a bank segments its default data in a credit 
card business unit by a number of borrower, loan, and product 
characteristics. Although the available internal historical evidence 
indicates a higher LGD, the bank judgmentally assigns a loss 
severity of 70 percent to a particular prime pool. The bank 
justifies this reduction in the LGD by claiming that it will do a 
better job of following policies for monitoring credit card 
performance in the future, for example, repricing accounts to 
generate more income and monitoring lines for problem accounts. Such 
an LGD adjustment is not appropriate because it is based on 
anticipated future performance rather than realized performance.

Example 7 From the LGD Estimation Standards (See Paragraphs 127-
129)

    Timing of Defaults and Recoveries.
    A bank measures recovery rates over time for a business line by 
loan characteristics. The recoveries are measured as an aggregate 
stream of cash inflows monthly or quarterly from all defaulted loans 
on book and not based on recoveries from a fixed group of defaulted 
loans. Collection costs are assessed as a proportion of the 
defaulted balances. Therefore loss severity rates are measured in 
the aggregate as:
[GRAPHIC] [TIFF OMITTED] TN27OC04.002

where all dollar values are measured concurrently.
    If defaulted balances are approximately constant over time, this 
method does not create any problems. However, when defaulted 
balances change over time, the bank should adjust for changes in the 
volume of defaulted accounts, since the use of recoveries from a 
prior group of defaulted accounts could underestimate the loss 
severity when aggregate defaulted balances were higher in a previous 
period, and overestimate them when defaulted balances were lower in 
a previous period.
    The following example demonstrates how the loss severity can be 
underestimated during periods of decreased defaulted balances when 
the loss severity is measured as the present defaulted balances 
minus recoveries from the previous period's defaulted balances 
(using a fixed 30 percent recovery rate) divided by the current 
period's defaulted balances.\*\

----------------------------------------------------------------------------------------------------------------
                                                         One-year               $Recoveries 30%   Measured loss
               Portfolio balances (EAD)                   default    Defaulted   net discounted  severity  (True
                                                           rate      balances    recovery rate     LGD  = 70%)
----------------------------------------------------------------------------------------------------------------
$1,000,000............................................       2.00%     $20,000           $6,000              70%
1,000,000.............................................        1.80      18,000            6,000               67
1,000,000.............................................        1.60      16,000            5,400               66
1,000,000.............................................        1.20      12,000            4,800               60
----------------------------------------------------------------------------------------------------------------


[[Page 62776]]

    Thus, while an accurate measure of LGD would remain constant at 
70% over the entire four-year period, this example shows how the use 
of the current year's defaulted balances, during a period when these 
balances are trending downward, leads to underestimates of LGD that 
grow more significant each year.

Example 8: The Effect of the Purchase Discount on EAD and LGD (see 
paragraph 138)

    Suppose a bank buys a QRE portfolio at a 5 percent discount. 
Assuming that PD and recoveries remain unchanged, EAD and LGD both 
change because of the discount. The discount does not act as a 
reserve against EL or as a capital offset against UL. For the 
purchasing bank, the newly purchased portfolio is initially put on 
the books (EAD) at the discounted price the bank paid. The EL and UL 
numbers would change from those of a portfolio bought or originated 
at par as follows:

 
------------------------------------------------------------------------
 
------------------------------------------------------------------------
Recoveries.....................................................      $50
Asset face value...............................................      100
Asset correlation..............................................        4
PD.............................................................        5
------------------------------------------------------------------------


------------------------------------------------------------------------
                                               No discount   5% discount
------------------------------------------------------------------------
EAD.........................................          $100           $95
Loss = EAD -recovery........................            50            45
LGD = Loss/EAD..............................          50.0          47.4
EL = PD x LGD x EAD.........................          2.50          2.25
UL (capital) per $ of EAD...................          4.87          4.61
IRB capital = UL per $ x EAD................          4.87          4.38
------------------------------------------------------------------------

List of Acronyms

    ALLL Allowance for loan and lease loss
    EAD Exposure at default
    EL Expected loss
    FFIEC Federal Financial Institutions Examination Council
    GAAP Generally Accepted Accounting Principles
    HELOC Home Equity Line of Credit
    IRB Advanced internal ratings-based approach (Basel II)
    K Unexpected loss capital requirement
    LEQ Loan equivalent exposure
    LGD Loss given default
    LTV Loan-to-value ratio
    MIS Management Information Systems
    PD Probability of default
    PMI Private Mortgage Insurance
    QIS Quantitative Impact Study
    QRE Qualifying revolving retail exposures
    R Asset value correlation (AVC)
    RS Retail Standard
    RWA Risk-weighted assets
    UL Unexpected loss

    Dated: October 15, 2004.
Julie L. Williams,
Acting Comptroller of the Currency.


    By order of the Board of Governors of the Federal Reserve 
System.

    Dated: October 15, 2004.
Jennifer J. Johnson,
Secretary of the Board.

    By order of the Board of Directors.


    Dated at Washington, DC, this day of October 18, 2004.
Robert E. Feldman,
Executive Secretary.

    By the Office of Thrift Supervision.

    Dated: October 14, 2004.
James T. Gilleran,
Director.

[FR Doc. 04-23771 Filed 10-26-04; 8:45 am]
BILLING CODE 4810-33-P; 6210-01-P; 6714-01-P; 6720-01-P