Clark R Abrahams, Mingyuan Zhang

Fair Lending Compliance

Intelligence and Implications for Credit Risk Management

• Gebundenes Buch

Produktbeschreibung

FAIR LENDING COMPLIANCE INTELLIGENCE AND IMPLICATIONS FOR CREDIT RISK MANAGEMENT Millions of Americans are unable to borrow from lending institutions largely because lenders do not have the proper credit information to prove an individual's willingness and ability to pay bills. An understanding of this fine line between credit access and credit risk is key to developing a new generation of models and processes that preserve safe and sound lending while promoting inclusiveness in the credit market. Part of the Wiley and SAS Business Series, Fair Lending Compliance: Intelligence and Implications for Credit Risk Management explores this overlap between fair lending and credit risk in order for lenders to provide greater and more affordable access to credit while operating within acceptable risk/return thresholds. With coverage of fair lending compliance specific to consumer and small business credit risk management, this innovative and timely work shows how various groups and organizations, as well as forward-thinking risk officers, can work to close the information gap for millions of Americans by maximizing the value of emerging nontraditional data sets for their institutions. Written for corporate executives, loan officers, compliance and credit risk managers, and information technology professionals, as well as lawyers, legislators, federal and state regulators, researchers, and academics, this book provides in-depth coverage of: * The dramatic changes in America's demographic and economic trends and how institutions can effectively respond to them and embrace their revenue potential * Fair lending compliance analysis methodology, components, and a strategic framework for approaching analysis * Advances in methodology including the universal performance indicator (UPI), dynamic conditional process (DCP), risk evaluation/policy formulation system (REPFS), multi-layered segmentation (MLS), and the credit and compliance optimization process (CCOP) Fair Lending Compliance provides coverage of traditional approaches coupled with several pioneering breakthroughs in methodology and technology that can enable all stakeholders to gain a broader and deeper understanding of fair lending analysis and develop more effective, more efficient, and better coordinated compliance self-assessment programs and credit risk management systems.

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Produktdetails

• Verlag: Wiley
• Seitenzahl: 384
• Erscheinungstermin: 1. Dezember 2007
• Englisch
• Abmessung: 260mm x 183mm x 25mm
• Gewicht: 907g
• ISBN-13: 9780470167762
• ISBN-10: 0470167769
• Artikelnr.: 23117455

Autorenporträt

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.

Inhaltsangabe

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