Details

<p>Foreword ix</p> <p>Preface xiii</p> <p>Acknowledgments xvii</p> <p><b>1 Credit Access and Credit Risk 1</b></p> <p>Enterprise Risk Management 2</p> <p>Laws and Regulations 4</p> <p>Changing Markets 6</p> <p>Prepare for the Challenges 8</p> <p>Return on Compliance 14</p> <p>Appendix 1A: Taxonomy of Enterprise Risks 17</p> <p>Appendix 1B: Making the Business Case 18</p> <p><b>2 Methodology and Elements of Risk and Compliance Intelligence 23</b></p> <p>Role of Data in Fair Lending Compliance Intelligence 23</p> <p>Sampling 29</p> <p>Types of Statistical Analysis 35</p> <p>Compliance Self-Testing Strategy Matrix 36</p> <p>Credit Risk Management Self-Testing Strategy Matrix 38</p> <p>Matching Appropriate Statistical Methods to Regulatory Examination Factors 42</p> <p>Case for a Systematic Approach 43</p> <p>Summary 44</p> <p>Appendix 2A: FFIEC Fair Lending Examination Factors within Seven Broad Categories 46</p> <p><b>3 Analytic Process Initiation 51</b></p> <p>Universal Performance Indicator 51</p> <p>Overall Framework 53</p> <p>Define Disparity 53</p> <p>Derive Indices 58</p> <p>Generate Universal Performance Indicator 65</p> <p>Performance Monitoring 75</p> <p>Summary 80</p> <p>Appendix 3A: UPI Application Example: Liquidity Risk Management 83</p> <p><b>4 Loan Pricing Analysis 85</b></p> <p>Understanding Loan Pricing Models 87</p> <p>Systematic Pricing Analysis Process 91</p> <p>Overage/Underage Analysis 112</p> <p>Overage/Underage Monitoring Overview 123</p> <p>Summary 125</p> <p>Appendix 4A: Pricing Analysis for HMDA Data 126</p> <p>Appendix 4B: Pricing and Loan Terms Adjustments 133</p> <p>Appendix 4C: Overage/Underage Data Model (Restricted to Input Fields, by Category) 137</p> <p>Appendix 4D: Detailed Overage/Underage Reporting 139</p> <p>Appendix 4E: Sample Size Determination 142</p> <p><b>5 Regression Analysis for Compliance Testing 147</b></p> <p>Traditional Main-Effects Regression Model Approach 148</p> <p>Dynamic Conditional Process 151</p> <p>DCP Modeling Framework 154</p> <p>DCP Application: A Simulation 168</p> <p>Summary 180</p> <p>Appendix 5A: Illustration of Bootstrap Estimation 181</p> <p><b>6 Alternative Credit Risk Models 183</b></p> <p>Credit Underwriting and Pricing 184</p> <p>Overview of Credit Risk Models 185</p> <p>Hybrid System Construction 201</p> <p>Hybrid System Maintenance 216</p> <p>Hybrid Underwriting Models with Traditional Credit Information 222</p> <p>Hybrid Underwriting Models with Nontraditional Credit Information 234</p> <p>Hybrid Models and Override Analysis 237</p> <p>Summary 248</p> <p>Appendix 6A: Loan Underwriting with Credit Scoring 250</p> <p>Appendix 6B: Log-Linear and Logistic Regression Models 254</p> <p>Appendix 6C: Additional Examples of Hybrid Models with Traditional Credit Information 255</p> <p>Appendix 6D: General Override Monitoring Process 265</p> <p><b>7 Multilayered Segmentation 267</b></p> <p>Segmentation Schemes Supporting Integrated Views 267</p> <p>Proposed Segmentation Approach 269</p> <p>Applications 275</p> <p>Summary 297</p> <p>Appendix 7A: Mathematical Underpinnings of BSM 298</p> <p>Appendix 7B: Data Element Examples for Dynamic Relationship Pricing Example 301</p> <p><b>8 Model Validation 305</b></p> <p>Model Validation for Risk and Compliance Intelligence 305</p> <p>Typical Model Validation Process, Methods, Metrics, and Components 307</p> <p>An Integrated Model Validation Approach 317</p> <p>Summary 344</p> <p>Closing Observations 344</p> <p>Index 347</p>

<p><b>CLARK ABRAHAMS</b> 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. <p><b>MINGYUAN ZHANG</b> 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.

<p><b>FAIR LENDING COMPLIANCE</b> <p><b>INTELLIGENCE AND IMPLICATIONS FOR CREDIT RISK MANAGEMENT</b> <p>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. <p>Part of the Wiley and SAS Business Series, <i>Fair Lending Compliance: Intelligence and Implications for Credit Risk Management</i> 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. <p>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: <ul> <li>The dramatic changes in America's demographic and economic trends and how institutions can effectively respond to them and embrace their revenue potential</li> <li>Fair lending compliance analysis methodology, components, and a strategic framework for approaching analysis</li> <li>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)</li> </ul> <p><i>Fair Lending Compliance</i> 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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