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Knowing the Score: New Report Offers Tour of Financial Data, Underwriting, and Marketing

We here at Robinson + Yu have just released a new report, Knowing the Score, which provides a guided tour of the complex and changing world of credit scoring. It’s designed to be the “missing manual” for policy professionals seeking to better understand technology’s impact on financial underwriting and marketing.

The word “scoring” is used a lot these days. For example, a widely quoted New York Times story described a new crop of “consumer evaluation or buying-power scores . . . [which are] highly valuable to companies that want—or in some cases, don’t want—to have you as their customer.” A recent report from Privacy International inventoried a variety of “consumer scores”—such as measurements of online social media influence. And industry regulators have acknowledged a “big fuzzy space” between how different kinds of financial assessments are viewed by the law.

We were left with many questions: What are the legal and practical differences between a “credit score” and a “marketing score”? Are credit scoring companies that rely on social networking data reliable? Should new forms of payment information (such as cable and utility bills) be sent to credit bureaus? Can new scoring methods bolster financial inclusion?

Our report adresses all of these questions, providing historical and legal context along the way.

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Data Flows and Scoring Models in the Consumer Credit Marketplace

The key takeaways are:

  • Financial advocates should seriously consider advancing the inclusion of “mainstream alternative data” (such as regular bill payments) into credit files. This new data, which often goes unreported today, could allow credit scores to be calculated for more people, enhancing access to the mainstream financial system. However, the impact of this new payment information on credit scores is hard to analyze without access to proprietary credit bureau data. Thus, we encourage further collaboration and transparency between advocates and industry. We also emphasize that utility payment data carries special risks: it must be reported carefully so as not to interfere with state consumer protection laws.
  • The predictiveness and fairness of new credit scores that rely on social network data and other nontraditional data sources (including, for example, how quickly a user scrolls through a terms of service document) is not yet proven. We predict that to the extent these new methods are actually adopted, they may struggle to comply with fair lending laws.
  • Today’s most widely used credit scoring methods (such as the approach used by FICO) are fair in the sense that they accurately reflect individuals’ credit risk across racial groups. Many studies have documented huge differences in average credit scores between racial groups. But the best available evidence, a 2007 study conducted by the Federal Reserve, indicates that mainstream scoring models themselves are not biased–that is to say, they accurately predict individual credit risk, for individuals of all races. This means that racial differences in average credit scores are a map of real, underlying inequalities, rather than a quirk of the scoring system. It also confirms that credit scores can be a powerful yardstick by which to measure the fairness of particular financial products and practices.
  • Marketing scores, built by credit bureaus from aggregated credit report data, can be used to target advertisements and change the appearance of websites as individuals navigate the web. These marketing scores, computed on a household or block level, segment individuals based on their financial health. They can come within a hair’s breadth of identifying a person, which would subject them to the Fair Credit Reporting Act, but they appear to be operating just outside the scope of that law. Unfortunately, technological constraints make it difficult to understand through outside observation what effect these scores are having. We urge regulators to play a fact-finding role to learn more about how this data is used.

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