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eBook - ePub
Credit Data and Scoring
The First Triumph of Big Data and Big Algorithms
Eric Rosenblatt
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- 274 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
eBook - ePub
Credit Data and Scoring
The First Triumph of Big Data and Big Algorithms
Eric Rosenblatt
Book details
Table of contents
Citations
About This Book
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.
- 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
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Information
Topic
ÉconomieSubtopic
Économie du développementTable of contents
- Cover image
- Title page
- Table of Contents
- Copyright
- Preface
- Chapter One. I used to have a reputation. Now I have a score.
- Chapter Two. The credit industry in the United States
- Chapter Three. My credit report—some of it is right
- Chapter Four. Credit reporting agencies: playing the long game
- Chapter Five. Data errors to keep money renters happy
- Chapter Six. Historic complaints about credit accuracy
- Chapter Seven. Differences in data between credit reporting agencies
- Chapter Eight. Can credit errors be fixed? Probably not
- Chapter nine. The mystery of credit scores
- Chapter Ten. Making a credit score: it starts with data
- Chapter Eleven. Picking the Y variable, picking the X variables
- Chapter Twelve. Calculating weight of evidence and information value
- Chapter Thirteen. Regressions
- Chapter Fourteen. Getting a sensible model
- Chapter Fifteen. Credit scores on the same borrower differ between CRAs
- Chapter Sixteen. The credit industry outside the United States
- Chapter Seventeen. List of practices by country
- Chapter Eighteen. Data security
- Chapter Nineteen. Algorithms and individuals
- Chapter Twenty. Something like a conclusion
- Appendix 1. U.S. (and a little bit of European) law
- Appendix 2. Final rule on credit scores from FHFA
- Appendix 3. Attachment: My credit report
- Appendix 4. A summary of your rights under the Fair Credit Reporting Act
- Appendix 5. Your rights under State law
- Index
Citation styles for Credit Data and Scoring
APA 6 Citation
Rosenblatt, E. (2020). Credit Data and Scoring ([edition unavailable]). Elsevier Science. Retrieved from https://www.perlego.com/book/1827528/credit-data-and-scoring-the-first-triumph-of-big-data-and-big-algorithms-pdf (Original work published 2020)
Chicago Citation
Rosenblatt, Eric. (2020) 2020. Credit Data and Scoring. [Edition unavailable]. Elsevier Science. https://www.perlego.com/book/1827528/credit-data-and-scoring-the-first-triumph-of-big-data-and-big-algorithms-pdf.
Harvard Citation
Rosenblatt, E. (2020) Credit Data and Scoring. [edition unavailable]. Elsevier Science. Available at: https://www.perlego.com/book/1827528/credit-data-and-scoring-the-first-triumph-of-big-data-and-big-algorithms-pdf (Accessed: 15 October 2022).
MLA 7 Citation
Rosenblatt, Eric. Credit Data and Scoring. [edition unavailable]. Elsevier Science, 2020. Web. 15 Oct. 2022.