62748 Federal Register / Vol. 69, No. 207 / Wednesday, October 27, 2004 / Notices 

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. 

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: 

regs.comments@occ.treas.gov.

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: 

regs.comments@occ.treas.gov. 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: 

regs.comments@federalreserve.gov.

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: comments@FDIC.gov.

• 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: 

regs.comments@ots.treas.gov. 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,

Federal Register / Vol. 69, No. 207 / Wednesday, October 27, 2004 / Notices 62749

1See 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.

2See 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.

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, 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.

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.

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. 

62750 Federal Register / Vol. 69, No. 207 / Wednesday, October 27, 2004 / Notices 

3 44 U.S.C. 3501 et seq.

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 segmentlevel 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.

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. 

Federal Register / Vol. 69, No. 207 / Wednesday, October 27, 2004 / Notices 62751

4Throughout this guidance, the term ‘‘banks’’

generally refers to banks, thrifts, and bank holding 

companies adopting the IRB approach.

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 

camille.dixon@occ.treas.gov.

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 

regs.comments@federalreserve.gov.

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 comments@fdic.gov.

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 

infocollection.comments@ots.treas.gov.

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.

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 

62752 Federal Register / Vol. 69, No. 207 / Wednesday, October 27, 2004 / Notices 

5This sentence is intended to capture bouncedcheck 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.

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 oneto 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.

• 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 

Federal Register / Vol. 69, No. 207 / Wednesday, October 27, 2004 / Notices 62753

6That 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.

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 supervisorspecified 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:

K LGD N

N PD R N

R

= × LGD PD ( ) + × ( )

−

















− × ( )





















− − 1 1 0 999

1

.

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:

Re e PD PD = ×− ( ) + × ( ) −× −× 0 03 1 0 16 35 35 . .

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 × K 

× EAD. 

18. The expected dollar loss on a 

segment (EL) is defined as PD × LGD ×

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 welldocumented 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 (backtesting). 

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 


EN27oc04.000</GPH> EN27oc04.001</GPH>
62754 Federal Register / Vol. 69, No. 207 / Wednesday, October 27, 2004 / Notices 

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-toincome 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 nondefaulted 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. 

Federal Register / Vol. 69, No. 207 / Wednesday, October 27, 2004 / Notices 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-toincome 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 welldocumented 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 

62756 Federal Register / Vol. 69, No. 207 / Wednesday, October 27, 2004 / Notices 

7A 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 × LGD × EAD will 

represent the dollar amount of expected losses (EL).

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.

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 

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

62758 Federal Register / Vol. 69, No. 207 / Wednesday, October 27, 2004 / Notices 

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 

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

62760 Federal Register / Vol. 69, No. 207 / Wednesday, October 27, 2004 / Notices 

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.

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-tovalue, 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.

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 creditrelated 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 

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

62762 Federal Register / Vol. 69, No. 207 / Wednesday, October 27, 2004 / Notices 

9This exemption applies to VA-guaranteed and 

FHA-insured mortgages.

10For all loans, the LGD calculation includes all 

unpaid interest and fees in the measure of economic 

loss.

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

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 existingportfolio 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 PMIcovered 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.

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 fixedhorizon 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. 

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

62764 Federal Register / Vol. 69, No. 207 / Wednesday, October 27, 2004 / Notices 

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 segmentwide 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, sLR/L ̄R ̄ must be 

‘‘relatively low,’’ where L ̄R ̄ is the 

average loss rate, and sLR 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 sLR to 

L ̄R ̄ . 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 modelbased. 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 

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

62766 Federal Register / Vol. 69, No. 207 / Wednesday, October 27, 2004 / Notices 

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-atdefault 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 

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

62768 Federal Register / Vol. 69, No. 207 / Wednesday, October 27, 2004 / Notices 

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 welldefined 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 

Federal Register / Vol. 69, No. 207 / Wednesday, October 27, 2004 / Notices 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 activities .................. A structure and system of management 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 oversight ........... Ultimate responsibility for the 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 

62770 Federal Register / Vol. 69, No. 207 / Wednesday, October 27, 2004 / Notices 

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; 

Federal Register / Vol. 69, No. 207 / Wednesday, October 27, 2004 / Notices 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 sevenyear 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 FirstLien 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, loanto-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-toincome 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. 

62772 Federal Register / Vol. 69, No. 207 / Wednesday, October 27, 2004 / Notices 

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-ofdollars-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.

Account

PD 

Average

balance

per

account 

Average

credit

line per

account 

Number

accounts in 

segment 

Total outstanding 

balance 

Total

undrawn

lines 

Estimated

percent

drawdown

(LEQ) 

Estimated 

EAD 

Gross

loss 

Gross

loss/EAD 

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.

Account

PD 

Average

balance

per

account 

Average

credit

line per

account 

Number

accounts in 

segment 

Total outstanding 

balance 

Total

undrawn

lines 

Estimated

percent

drawdown

(LEQ) 

Estimated 

EAD 

Gross

loss 

Gross

loss/EAD 

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

Account 

PD 

Average

balance per 

account 

Average

credit line 

per account 

Number

accounts in 

segment 

Total outstanding 

balance 

Total

undrawn

lines 

Estimated

percent

drawdown

(LEQ) 

Estimated 

EAD 

Gross

loss 

Gross

loss/

EAD 

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% 

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

Number

total

accounts 

Number

defaulted

accounts 

Account

PD 

Total

beginning

outstanding

balances 

Average

beginning

balance

non-defaulted

accounts 

Average

beginning

balance

defaulted

accounts 

Total

gross

losses 

Gross Losses/

beginning

outstanding

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 oneyear-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: 

62774 Federal Register / Vol. 69, No. 207 / Wednesday, October 27, 2004 / Notices 

11The 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.

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

Time Superprime

(percent) 

Prime

(percent) 

Subprime

(percent) 

Default

(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.

Annual portfolio growth rate 

Total portfolio accounts Accounts

defaulted

by end

of year 

PD front

start of year 

portfolio 

PD from end 

of year portfolio

(percent) Start of year End of year 

¥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. 

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

LGD defaulted balances discounted

defaulted = ( − )

( )

 recoveries net of collections costs

balances

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.*

Portfolio balances (EAD) One-year 

default rate 

Defaulted 

balances 

$Recoveries 

30% net discounted recovery rate 

Measured loss 

severity 

(True 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 


EN27OC04.002</GPH>
62776 Federal Register / Vol. 69, No. 207 / Wednesday, October 27, 2004 / Notices 

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 × LGD × EAD ................................................................................................................................................ 2.50 2.25 

UL (capital) per $ of EAD ............................................................................................................................................ 4.87 4.61 

IRB capital = UL per $ × 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