Credit Data and Scoring: The First Triumph of Big Data and Big Algorithms

Author:   Eric Rosenblatt (Fannie Mae, Washington DC, USA) ,  Rosenblatt
Publisher:   Elsevier Science Publishing Co Inc
ISBN:  

9780128188156


Pages:   274
Publication Date:   07 January 2020
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Credit Data and Scoring: The First Triumph of Big Data and Big Algorithms


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Overview

Credit Data and Scoring: The First Triumph of Big Data and Big Algorithms illuminates the often-hidden practice of predicting an individual’s economic responsibility. Written by a leading practitioner, it examines the international implications of US leadership in credit scoring and what other countries have learned from it in building their own systems. Through its comprehensive contemporary perspective, the book also explores how algorithms and big data are driving the future of credit scoring. By revealing a new big picture and data comparisons, it delivers useful insights into legal, regulatory and data manipulation.

Full Product Details

Author:   Eric Rosenblatt (Fannie Mae, Washington DC, USA) ,  Rosenblatt
Publisher:   Elsevier Science Publishing Co Inc
Imprint:   Academic Press Inc
Weight:   0.520kg
ISBN:  

9780128188156


ISBN 10:   0128188154
Pages:   274
Publication Date:   07 January 2020
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

1. When Our Reputation Became our Score 2. The Credit Industry 3. CRAs - Losing Battles to Win the War 4. My Credit Report 5. Historic Complaints about Credit Accuracy 6. Differences in Credit Data Between Bureaus 7. Differences in Credit Scores between Bureaus 8. The Mystery of Credit Scores 9. Making a Credit Score 10. Picking the y Variable, Picking the x Variables 11. Calculating Weight of Evidence and Information Value 12. Regressions 13. Getting a Good Model 14. Data Flows: The Road to Attributes and Scores 15. The War Between Individuals and Algorithms 16. Protecting Data 17. About the Authors Appendix 1. Credit Laws / Data Laws 2. My Credit Report

Reviews

Explores credit scoring and personal data collection by credit reporting agencies (CRAs), highlighting the arbitrariness, unfairness, and inaccuracy of the practice. Chronicles how data-based credit scores replaced subjective and reputational qualities as the basis of credit decisions. Provides an overview of the credit industry in the United States and its regulators. --Journal of Economic Literature


""Explores credit scoring and personal data collection by credit reporting agencies (CRAs), highlighting the arbitrariness, unfairness, and inaccuracy of the practice. Chronicles how data-based credit scores replaced subjective and reputational qualities as the basis of credit decisions. Provides an overview of the credit industry in the United States and its regulators."" --Journal of Economic Literature


Author Information

Eric Rosenblatt has worked in the mortgage industry, mainly the credit side, for thirty years, most of it (since 2000) as a Vice President at Fannie Mae. He received a Ph.D. in Finance in 1994. At Fannie Mae he was known for his management of Credit Risk analytics (including credit report models), his correct call of the housing recession, and for what Fannie called Innovation: applications and models which integrated data and made credit, fraud, and home valuation decisions. One of these applications, Collateral Underwriter, is used by all lenders and appraisal management companies. Another is a credit scoring model that treats people that pay their credit card balances (transactors) differently than people that do not (revolvers). While at Fannie Mae he published 19 papers and was granted 13 patents.

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