Skip to main content
Elsevier

Credit Data and Scoring

The First Triumph of Big Data and Big Algorithms

  • 1st Edition - January 7, 2020
  • Latest edition
  • Author: Eric Rosenblatt
  • Language: English

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 b… Read more

Data Mining & ML

Unlock the cutting edge

Up to 20% on trusted resources. Build expertise with data mining, ML methods.

Explore now

Description

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.

Key features

  • Provides insights into credit scoring goals and methods
  • Examines U.S leadership in developing credit data and algorithms and how other countries depart from it
  • Analyzes the growing influence of algorithms in data scoring

Readership

Upper-division undergraduates, graduate students, and professionals worldwide working on subjects related to economic development and growth

Table of contents

1. When Our Reputation Became our Score2. The Credit Industry3. CRAs - Losing Battles to Win the War4. My Credit Report5. Historic Complaints about Credit Accuracy6. Differences in Credit Data Between Bureaus7. Differences in Credit Scores between Bureaus8. The Mystery of Credit Scores9. Making a Credit Score10. Picking the y Variable, Picking the x Variables11. Calculating Weight of Evidence and Information Value12. Regressions13. Getting a Good Model14. Data Flows: The Road to Attributes and Scores15. The War Between Individuals and Algorithms16. Protecting Data17. About the Authors

Appendix1. Credit Laws / Data Laws2. My Credit Report

Review quotes

"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

Product details

  • Edition: 1
  • Latest edition
  • Published: January 7, 2020
  • Language: English

About the author

ER

Eric Rosenblatt

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.
Affiliations and expertise
Fannie Mae, Washington DC, USA

View book on ScienceDirect

Read Credit Data and Scoring on ScienceDirect