Big Data: A Big Disappointment for Scoring Consumer Credit Risk

Evaluating the promise of big data to lead to better products/services for the unbanked/underbanked

This paper presents the results of an examination carried out to understand whether big data is really fulfilling its promise of making better predictive algorithms that in turn can make better products available to the unbanked and underbanked. It provides a description of how the big data ecosystem works, studies the accuracy of big data, evaluates the discriminatory impact of big data scores, and analyzes whether big data actually leads to better products. The paper concludes that big data doesn't live up to its promises on the basis of a review of the underwriting systems and the small consumer loans that use them. It provides the following federal policy recommendations:

  • Federal Trade Commission (FTC) should continue to study big data brokers and credit scores testing for potential discriminatory impact, compliance with disclosure requirements, accuracy, and the predictiveness of the algorithms;
  • FTC and the Consumer Financial Protection Bureau (CFPB) should examine big data brokers for legal compliance with FCRA and Equal Credit Opportunity Act (ECOA);
  • CFPB should create a mandatory registry for consumer reporting agencies so that consumers can know who has their data;
  • CFPB should require all of the financial products it regulates to meet Regulation B?s requirements for credit scoring models.

About this Publication

By Yu, P., McLaughlin, J. , Levy, M.