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Big Data: A Big Disappointment for Scoring Consumer Credit Risk

Big Data: A Big Disappointment for Scoring Consumer Credit Risk

Big data promises to make better predictive algorithms that in turn can make better products available to the unbanked and underbanked. Affordable access to credit is of vital importance to the economic well-being of these consumers. But does big data live up to its big promises? NCLC's analysis concludes that as data about consumers proliferates, so does bad data. And, big data scores may violate federal laws.


Executive Summary

NCLC's Study of Big Data Accuracy

Press Release

Revised: 3/17/2014, ©National Consumer Law Center, Inc.

Related Publications

Fair Credit Reporting 
Credit Discrimination

This report builds on NCLC's advocacy, training, publications, and public policy work on consumer privacy issues. to promote family financial well-being. Learn more about NCLC's work in this area.


About 64 million people in the United States have no credit history or lack sufficient credit history to generate a credit score, limiting access to traditional banking services. Big data brokers promise to use information culled from Internet searches, social media, and mobile apps to help lenders make decisions as to creditworthiness of individuals. The resulting data and lending products may provide affordable access to credit for these individuals or they may be a means of preying on vulnerable communities.

When analyzing the use of big data, consumers and policy makers should ask:

1. Are the decisions based upon accurate data?

2. Can the algorithms, when fed with good data, actually predict the creditworthiness of low-income consumers?

3. Does the use of big data in reports used for credit, employment, insurance, and other purposes comply with consumer protection laws?

4. Is there the potential for a discriminatory impact on racial, geographic, or other minority groups?

5. Does the use of big data actually improve the choices for consumers?

NCLC's Study of Big Data Accuracy

Expanding the number of data points also introduces the risk that inaccuracies will play a greater role in determining creditworthiness. Given these indications of accuracy problems, we conducted our own survey for this report of the data maintained on consumers by big data brokers. Even given our initial skepticism, we were astonished by the scope of inaccuracies among the data brokers we investigated. In general, obtaining the data was challenging and the reports our volunteers received were riddled with inaccuracies. Errors ranged from the mundane—a wrong e-mail address or incorrect phone number—to seriously flawed.

Big Data Loan Products

NCLC also analyzed 7 loan products based on big data underwriting. MySalaryLine, Plain Green, Great Plains Lending, Presta, and RISE (uses ThinkFinance technology); Spotloan (uses ZestFinance technology); and LendUp uses its own big data infrastructure.

Six of these products present themselves as payday loan alternatives, yet, like payday loans, carry triple-digit annual percentage rates. Even more troubling: All of the lenders, except Presta and MySalaryLine, require borrowers to provide sensitive banking information (i.e. bank name, routing number, and account number). A lender could potentially use this information to reach into a bank account and take the funds if the consumer fails to make a payment. The requirement for electronic information is of concern and may be an attempt to obtain access to the consumer's account while evading the important protections of the Electronic Funds Transfer Act. The requirement that the borrower provide bank account information could ensure that the lender will be repaid, even if the borrower is unable to afford the loan without neglecting other expenses (like rent or food) or falling into a cycle of debt.

Potential Federal Legal Violations

Electronic Funds Transfer Act (EFTA) All of the lenders, except Presta and MySalaryLine, require borrowers to provide sensitive banking information (i.e. bank name, routing number, and account number). The Electronic Funds Transfer Act generally prohibits conditioning an extension of credit on the consumer's repayment of that debt by preauthorized electronic fund transfers. Many payday-type lenders structure their loan products to evade the important protections of this Act while still maintaining a high degree of access to the consumer's account, and we are concerned that the lenders discussed in this report that require bank account information may be following this pattern.

Federal Trade Commission (FTC) Credit Practices Rule which prohibits creditors from using certain contract provisions that the FTC found to be unfair to consumers.

Equal Credit Opportunity Act (ECOA) The ECOA prohibits racial discrimination in the granting of credit. It prohibits not just disparate treatment but also policies or practices that have a disparate impact. Big data broker marketing materials judge consumers based upon data generated from their Internet usage, mobile applications and social media. Access and usage of these sources vary by race and socioeconomic status so any algorithm based upon them may have racial disparities.

Fair Credit Reporting Act (FCRA) Many of the big data brokers could be considered consumer reporting agencies and thus subject to the FCRA. Three of the most important functions of the FCRA deal with accuracy, disclosure, and the right to dispute items on a credit report. Given the size of the data set and the sources of information, it is highly unlikely that the companies that provide the big data analytics and the users of that data meet these requirements.

Key Recommendations

arrowThe 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.

arrowThe FTC and the Consumer Financial Protection Bureau (CFPB) should examine big data brokers for legal compliance with FCRA and Equal Credit Opportunity Act (ECOA).

arrowThe CFPB should create a mandatory registry for consumer reporting agencies so that consumers can know who has their data.

arrowThe CFPB, in coordination with the FTC, should create regulations based upon the FTC's research that:

indent1Define reasonable procedures for ensuring accuracy when using big data;

indent1Specify a mechanism so that consumers can do a meaningful review of their files including all data points that can be linked to that consumer (not just those that identify the consumer explicitly); and

indent1Define reasonable procedures for disputing the accuracy of information.

arrowThe CFPB should require all of the financial products it regulates to meet ECOA's Regulation B requirements for credit scoring models.