{"product_id":"fair-lending-compliance-isbn-9780470167762","title":"Fair Lending Compliance","description":"Praise for\u003cbr\u003e \u003cbr\u003e Fair Lending ComplianceIntelligence and Implications for Credit Risk Management\u003cbr\u003e \u003cbr\u003e \"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.\"\u003cbr\u003e --The Honorable Rodney E. Hood\u003cbr\u003e \u003cbr\u003e \"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.\"\u003cbr\u003e --Frank A. Hirsch Jr., Nelson Mullins Riley \u0026amp; Scarborough LLP Financial Services Attorney and former general counsel for Centura Banks, Inc.\u003cbr\u003e \u003cbr\u003e \"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.\" \u003cbr\u003e --Alyssa Stewart Lee, Deputy Director, Urban Markets Initiative The Brookings Institution\u003cbr\u003e \u003cbr\u003e \"[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.\"\u003cbr\u003e --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 \u003cp\u003eForeword ix\u003c\/p\u003e \u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003eAcknowledgments xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Credit Access and Credit Risk 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEnterprise Risk Management 2\u003c\/p\u003e \u003cp\u003eLaws and Regulations 4\u003c\/p\u003e \u003cp\u003eChanging Markets 6\u003c\/p\u003e \u003cp\u003ePrepare for the Challenges 8\u003c\/p\u003e \u003cp\u003eReturn on Compliance 14\u003c\/p\u003e \u003cp\u003eAppendix 1A: Taxonomy of Enterprise Risks 17\u003c\/p\u003e \u003cp\u003eAppendix 1B: Making the Business Case 18\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Methodology and Elements of Risk and Compliance Intelligence 23\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRole of Data in Fair Lending Compliance Intelligence 23\u003c\/p\u003e \u003cp\u003eSampling 29\u003c\/p\u003e \u003cp\u003eTypes of Statistical Analysis 35\u003c\/p\u003e \u003cp\u003eCompliance Self-Testing Strategy Matrix 36\u003c\/p\u003e \u003cp\u003eCredit Risk Management Self-Testing Strategy Matrix 38\u003c\/p\u003e \u003cp\u003eMatching Appropriate Statistical Methods to Regulatory Examination Factors 42\u003c\/p\u003e \u003cp\u003eCase for a Systematic Approach 43\u003c\/p\u003e \u003cp\u003eSummary 44\u003c\/p\u003e \u003cp\u003eAppendix 2A: FFIEC Fair Lending Examination Factors within Seven Broad Categories 46\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Analytic Process Initiation 51\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUniversal Performance Indicator 51\u003c\/p\u003e \u003cp\u003eOverall Framework 53\u003c\/p\u003e \u003cp\u003eDefine Disparity 53\u003c\/p\u003e \u003cp\u003eDerive Indices 58\u003c\/p\u003e \u003cp\u003eGenerate Universal Performance Indicator 65\u003c\/p\u003e \u003cp\u003ePerformance Monitoring 75\u003c\/p\u003e \u003cp\u003eSummary 80\u003c\/p\u003e \u003cp\u003eAppendix 3A: UPI Application Example: Liquidity Risk Management 83\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Loan Pricing Analysis 85\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding Loan Pricing Models 87\u003c\/p\u003e \u003cp\u003eSystematic Pricing Analysis Process 91\u003c\/p\u003e \u003cp\u003eOverage\/Underage Analysis 112\u003c\/p\u003e \u003cp\u003eOverage\/Underage Monitoring Overview 123\u003c\/p\u003e \u003cp\u003eSummary 125\u003c\/p\u003e \u003cp\u003eAppendix 4A: Pricing Analysis for HMDA Data 126\u003c\/p\u003e \u003cp\u003eAppendix 4B: Pricing and Loan Terms Adjustments 133\u003c\/p\u003e \u003cp\u003eAppendix 4C: Overage\/Underage Data Model (Restricted to Input Fields, by Category) 137\u003c\/p\u003e \u003cp\u003eAppendix 4D: Detailed Overage\/Underage Reporting 139\u003c\/p\u003e \u003cp\u003eAppendix 4E: Sample Size Determination 142\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Regression Analysis for Compliance Testing 147\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTraditional Main-Effects Regression Model Approach 148\u003c\/p\u003e \u003cp\u003eDynamic Conditional Process 151\u003c\/p\u003e \u003cp\u003eDCP Modeling Framework 154\u003c\/p\u003e \u003cp\u003eDCP Application: A Simulation 168\u003c\/p\u003e \u003cp\u003eSummary 180\u003c\/p\u003e \u003cp\u003eAppendix 5A: Illustration of Bootstrap Estimation 181\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Alternative Credit Risk Models 183\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCredit Underwriting and Pricing 184\u003c\/p\u003e \u003cp\u003eOverview of Credit Risk Models 185\u003c\/p\u003e \u003cp\u003eHybrid System Construction 201\u003c\/p\u003e \u003cp\u003eHybrid System Maintenance 216\u003c\/p\u003e \u003cp\u003eHybrid Underwriting Models with Traditional Credit Information 222\u003c\/p\u003e \u003cp\u003eHybrid Underwriting Models with Nontraditional Credit Information 234\u003c\/p\u003e \u003cp\u003eHybrid Models and Override Analysis 237\u003c\/p\u003e \u003cp\u003eSummary 248\u003c\/p\u003e \u003cp\u003eAppendix 6A: Loan Underwriting with Credit Scoring 250\u003c\/p\u003e \u003cp\u003eAppendix 6B: Log-Linear and Logistic Regression Models 254\u003c\/p\u003e \u003cp\u003eAppendix 6C: Additional Examples of Hybrid Models with Traditional Credit Information 255\u003c\/p\u003e \u003cp\u003eAppendix 6D: General Override Monitoring Process 265\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Multilayered Segmentation 267\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSegmentation Schemes Supporting Integrated Views 267\u003c\/p\u003e \u003cp\u003eProposed Segmentation Approach 269\u003c\/p\u003e \u003cp\u003eApplications 275\u003c\/p\u003e \u003cp\u003eSummary 297\u003c\/p\u003e \u003cp\u003eAppendix 7A: Mathematical Underpinnings of BSM 298\u003c\/p\u003e \u003cp\u003eAppendix 7B: Data Element Examples for Dynamic Relationship Pricing Example 301\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Model Validation 305\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eModel Validation for Risk and Compliance Intelligence 305\u003c\/p\u003e \u003cp\u003eTypical Model Validation Process, Methods, Metrics, and Components 307\u003c\/p\u003e \u003cp\u003eAn Integrated Model Validation Approach 317\u003c\/p\u003e \u003cp\u003eSummary 344\u003c\/p\u003e \u003cp\u003eClosing Observations 344\u003c\/p\u003e \u003cp\u003eIndex 347\u003c\/p\u003e   \u003cp\u003e\u003cb\u003eCLARK ABRAHAMS\u003c\/b\u003e 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.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eMINGYUAN ZHANG\u003c\/b\u003e 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.    \u003c\/p\u003e\u003cp\u003e\u003cb\u003eFAIR LENDING COMPLIANCE\u003c\/b\u003e  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eINTELLIGENCE AND IMPLICATIONS FOR CREDIT RISK MANAGEMENT\u003c\/b\u003e  \u003c\/p\u003e\u003cp\u003eMillions 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.  \u003c\/p\u003e\u003cp\u003ePart of the Wiley and SAS Business Series, \u003ci\u003eFair Lending Compliance: Intelligence and Implications for Credit Risk Management\u003c\/i\u003e 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.  \u003c\/p\u003e\u003cp\u003eWritten 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:  \u003c\/p\u003e\u003cul\u003e \u003cli\u003eThe dramatic changes in America's demographic and economic trends and how institutions can effectively respond to them and embrace their revenue potential\u003c\/li\u003e \u003cli\u003eFair lending compliance analysis methodology, components, and a strategic framework for approaching analysis\u003c\/li\u003e \u003cli\u003eAdvances 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)\u003c\/li\u003e \u003c\/ul\u003e  \u003cp\u003e\u003ci\u003eFair Lending Compliance\u003c\/i\u003e 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.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989195112677,"sku":"NP9780470167762","price":90.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470167762.jpg?v=1761783163","url":"https:\/\/k12savings.com\/es\/products\/fair-lending-compliance-isbn-9780470167762","provider":"K12savings","version":"1.0","type":"link"}