公平放贷：信贷风险管理Fair Lending Compliance : Intelligence and Implications for Credit Risk Management
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作者： C. R. Abrahams等著
出 版 社：
出版时间： 2008-1-1字数：版次： 1页数： 357印刷时间： 2008/01/01开本： 16开印次： 1纸张： 胶版纸I S B N ： 9780470167762包装： 精装内容简介
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
作者简介
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 Americaand 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 telecommunicationsconsulting firm.
目录
Foreword
Preface
1 Credit Access and Credit Risk
Enterprise Risk Management
Laws and Regulations
Changing Markets
Prepare for the Challenges
Return on Compliance
Appendix 1A: Taxonomy of Enterprise Risks
Appendix 1B: Making the Business Case
2 Methodology and Elements of Risk and Compliance Intelligence
Role of Data in Fair Lending Compliance Intelligence
Sampling
Types of Statistical Analysis
Compliance Self-Testing Strategy Matrix
Credit Risk Management Self-Testing Strategy Matrix
Matching Appropriate Statistical Methods to Regulatory Examination Factors
Case for a Systematic Approach
Summary
Appendix 2A: FFIEC Fair Lending Examination Factors within Seven Broad Categories
3 Analytic Process Initiation
Universal Performance Indicator
Overall Framework
Define Disparity
Derive Indices
Generate Universal Performance Indicator
Performance Monitoring
Summary
Appendix 3A: UPI Application Example: Liquidity Risk Management
4 Loan Pricing Analysis
Understanding Loan Pricing Models
Systematic Pricing Analysis Process
Overage/Underage Analysis
Overage/Underage Monitoring Overview
Summary
Appendix 4A: Pricing Analysis for HMDA Data
Appendix 4B: Pricing and Loan Terms Adjustments
Appendix 4C: Overage/Underage Data Model (Restricted to Input Fields, by Category)
Appendix 4D: Detailed Overage/Underage Reporting
Appendix 4E: Sample Size Determination
Regression Analysis for Compliance Testing
Traditional Main-Effects Regression Model Approach
Dynamic Conditional Process
DCP Modeling Framework
DCP Application: A Simulation
Summary
Appendix 5A: Illustration of Bootstrap Estimation
6 Alternative Credit Risk Models
Credit Underwriting and Pricing
Overview of Credit Risk Models
Hybrid System Construction
Hybrid System Maihtenance
Hybrid Underwriting Models with Traditional Credit Information
Hybrid Underwriting Models with Nontraditional Credit
Information
Hybrid Models and Override Analysis
Summary
Appendix 6A: Loan Underwriting with Credit Scoring
Appendix 6B: Log-Linear and Logistic Regression Models
Appendix 6C: Additional Examples of Hybrid Models with Traditional Credit
Information
Appendix 6D: General Override Monitoring Process
7 Multilayered Segmentation
Segmentation Schemes Supporting Integrated Views
Proposed Segmentation Approach
Applications
Summary
Appendix 7A: Mathematical Underpinnings of BSM
Appendix 7B: Data Element Examples for Dynamic Relationship Pricing Example
8 Model Validation
Index
