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More About This Title Fair Lending Compliance: Intelligence and Implications for Credit Risk Management
English
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
English
CLARK ABRAHAMS is the Director for Fair Banking at SAS, where he leads business and product development. He has over thirty years of experience in the financial services industry, at corporations including Bank of America and Fair Isaac Corporation.
MINGYUAN ZHANG is Solutions Architect for SAS Financial Services. Over the last 10 years with SAS Institute, he has successfully developed and implemented many economic forecasting, data mining, and financial risk management solutions for various industries. Prior to joining SAS, he served as an economic and financial analyst for a leading telecommunications consulting firm.
English
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
