By Clark R. Abrahams
reasonable Lending ComplianceIntelligence and Implications for credits threat Management
"Brilliant and informative. An in-depth examine cutting edge techniques to credits probability administration written through practitioners. This booklet will function a necessary reference textual content should you desire to make credits available to underserved shoppers. it's accomplished and obviously written."
--The Honorable Rodney E. Hood
"Abrahams and Zhang's well timed treatise is a must-read for all these attracted to the serious function of credits within the economic climate. They ably discover the intersection of credits entry and credits threat, suggesting a hybrid technique of human judgment and machine versions because the priceless route to balanced and reasonable lending. In an atmosphere of quickly altering buyer demographics, in addition to regulatory reform projects, this booklet indicates new analytical versions in which to supply credits to make sure compliance and to control company risk."
--Frank A. Hirsch Jr., Nelson Mullins Riley & Scarborough LLP monetary companies legal professional and previous normal assistance for Centura Banks, Inc.
"This ebook tackles head out there disasters that our present hazard administration platforms have to deal with. not just do Abrahams and Zhang adeptly articulate why we will be able to and will enhance our platforms, they supply the analytic facts, and the stairs towards implementations. reasonable Lending Compliance fills a much-needed hole within the box. If applied systematically, this concept management will result in advancements in reasonable lending practices for all Americans."
--Alyssa Stewart Lee, Deputy Director, city Markets Initiative The Brookings Institution
"[Fair Lending Compliance]...provides a distinct mix of qualitative and quantitative suggestions to 2 forms of monetary associations: those who simply want a little assist in staying at the correct facet of complicated reasonable housing rules; and people who aspire to management in profitably and responsibly serving the unmet credits wishes of various companies and shoppers in America's rising household markets."
--Michael A. Stegman, PhD, the toilet D. and Catherine T. MacArthur origin, Duncan MacRae '09 and Rebecca Kyle MacRae Professor of Public coverage Emeritus, collage of North Carolina at Chapel Hill
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Additional resources for Fair lending compliance : intelligence and implications for credit risk management
Company Internal Data These data relate primarily to borrower credit application–based qualification factors such as income, net worth, current monthly housing payment, years on job, years at current address, and so on. In addition, information on borrower credit history is obtained from as many as three different bureaus and merged into a single performance record that tracks delinquent behavior across revolving, installment, and mortgage trade lines by frequency and age and also captures foreclosures, repossessions, judgments, liens, and bankruptcy occurrences over time.
We describe the compliance data by source, and how to prepare the data for analysis. Next, we introduce typical sampling methodologies and process that are used to develop unbiased and efficient data collection procedures for compliance analysis. The importance of capturing associated lending policy business rules is underscored and illustrated. We then discuss types of statistical analysis and provide a case for a systematic analysis approach. 1 The strategy matrix appearing at the end of the chapter maps the most appropriate statistical methods to those 49 factors.
The general idea is that pricing exposures are a function of monetary damages, the cost of policy and procedure changes, staff training, regulatory fines and greater oversight burden, direct legal defense and court costs, consequences of damaged reputation, and so forth. Isolating compensatory monetary damages, we find that these expenses are a function of the injured class definition, and the loss definition. The injured class and loss definitions are based on the allegation that entails the amount of points and interest differential and the incidence by product type, geographic area, and time period.