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Fair Lending Compliance
Intelligence and Implications for Credit Risk Management
Clark R. Abrahams (Author), Mingyuan Zhang (Author)
9780470167762, Wiley
Hardback, published 5 February 2008
384 pages, Exhibits: 229 B&W, 0 Color
26 x 18.8 x 3.1 cm, 0.821 kg
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
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
Subject Areas: Finance & accounting [KF]
