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Fair Lending Compliance: Intelligence and Implications for Credit Risk Management



Fair Lending Compliance: Intelligence and Implications for Credit Risk Management

Clark R. Abrahams, Mingyuan Zhang

ISBN: 978-0-470-16776-2 January 2008 384 Pages


Praise for

Fair Lending ComplianceIntelligence and Implications for Credit Risk Management

"Brilliant and informative. An in-depth look at innovative approaches to credit risk management written by industry practitioners. This publication will serve as an essential reference text for those who wish to make credit accessible to underserved consumers. It is comprehensive and clearly written."
--The Honorable Rodney E. Hood

"Abrahams and Zhang's timely treatise is a must-read for all those interested in the critical role of credit in the economy. They ably explore the intersection of credit access and credit risk, suggesting a hybrid approach of human judgment and computer models as the necessary path to balanced and fair lending. In an environment of rapidly changing consumer demographics, as well as regulatory reform initiatives, this book suggests new analytical models by which to provide credit to ensure compliance and to manage enterprise risk."
--Frank A. Hirsch Jr., Nelson Mullins Riley & Scarborough LLP Financial Services Attorney and former general counsel for Centura Banks, Inc.

"This book tackles head on the market failures that our current risk management systems need to address. Not only do Abrahams and Zhang adeptly articulate why we can and should improve our systems, they provide the analytic evidence, and the steps toward implementations. Fair Lending Compliance fills a much-needed gap in the field. If implemented systematically, this thought leadership will lead to improvements in fair lending practices for all Americans."
--Alyssa Stewart Lee, Deputy Director, Urban Markets Initiative The Brookings Institution

"[Fair Lending Compliance]...provides a unique blend of qualitative and quantitative guidance to two kinds of financial institutions: those that just need a little help in staying on the right side of complex fair housing regulations; and those that aspire to industry leadership in profitably and responsibly serving the unmet credit needs of diverse businesses and consumers in America's emerging domestic markets."
--Michael A. Stegman, PhD, The John D. and Catherine T. MacArthur Foundation, Duncan MacRae '09 and Rebecca Kyle MacRae Professor of Public Policy Emeritus, University of North Carolina at Chapel Hill

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Foreword ix

Preface xiii

Acknowledgments xvii

1 Credit Access and Credit Risk 1

Enterprise Risk Management 2

Laws and Regulations 4

Changing Markets 6

Prepare for the Challenges 8

Return on Compliance 14

Appendix 1A: Taxonomy of Enterprise Risks 17

Appendix 1B: Making the Business Case 18

2 Methodology and Elements of Risk and Compliance Intelligence 23

Role of Data in Fair Lending Compliance Intelligence 23

Sampling 29

Types of Statistical Analysis 35

Compliance Self-Testing Strategy Matrix 36

Credit Risk Management Self-Testing Strategy Matrix 38

Matching Appropriate Statistical Methods to Regulatory Examination Factors 42

Case for a Systematic Approach 43

Summary 44

Appendix 2A: FFIEC Fair Lending Examination Factors within Seven Broad Categories 46

3 Analytic Process Initiation 51

Universal Performance Indicator 51

Overall Framework 53

Define Disparity 53

Derive Indices 58

Generate Universal Performance Indicator 65

Performance Monitoring 75

Summary 80

Appendix 3A: UPI Application Example: Liquidity Risk Management 83

4 Loan Pricing Analysis 85

Understanding Loan Pricing Models 87

Systematic Pricing Analysis Process 91

Overage/Underage Analysis 112

Overage/Underage Monitoring Overview 123

Summary 125

Appendix 4A: Pricing Analysis for HMDA Data 126

Appendix 4B: Pricing and Loan Terms Adjustments 133

Appendix 4C: Overage/Underage Data Model (Restricted to Input Fields, by Category) 137

Appendix 4D: Detailed Overage/Underage Reporting 139

Appendix 4E: Sample Size Determination 142

5 Regression Analysis for Compliance Testing 147

Traditional Main-Effects Regression Model Approach 148

Dynamic Conditional Process 151

DCP Modeling Framework 154

DCP Application: A Simulation 168

Summary 180

Appendix 5A: Illustration of Bootstrap Estimation 181

6 Alternative Credit Risk Models 183

Credit Underwriting and Pricing 184

Overview of Credit Risk Models 185

Hybrid System Construction 201

Hybrid System Maintenance 216

Hybrid Underwriting Models with Traditional Credit Information 222

Hybrid Underwriting Models with Nontraditional Credit Information 234

Hybrid Models and Override Analysis 237

Summary 248

Appendix 6A: Loan Underwriting with Credit Scoring 250

Appendix 6B: Log-Linear and Logistic Regression Models 254

Appendix 6C: Additional Examples of Hybrid Models with Traditional Credit Information 255

Appendix 6D: General Override Monitoring Process 265

7 Multilayered Segmentation 267

Segmentation Schemes Supporting Integrated Views 267

Proposed Segmentation Approach 269

Applications 275

Summary 297

Appendix 7A: Mathematical Underpinnings of BSM 298

Appendix 7B: Data Element Examples for Dynamic Relationship Pricing Example 301

8 Model Validation 305

Model Validation for Risk and Compliance Intelligence 305

Typical Model Validation Process, Methods, Metrics, and Components 307

An Integrated Model Validation Approach 317

Summary 344

Closing Observations 344

Index 347