FTC Highlights Alternative Scoring Products in Privacy Seminar
Last week, the Federal Trade Commission (“FTC”) hosted a panel discussion on Alternative Scoring Products examining the scope and effects of predictive scores. The discussion highlighted growing concerns over the use and accuracy of predictive analytics to determine consumers’ access to products and offers. In the context of alternative scores, predictive analytics involve techniques to analyze and compile online and offline consumer data to develop digital profiles and predict consumer behavior. Similar to how credit scores predict credit worthiness, these alternative predictive scores are used to forecast various trends, such as the likelihood that a person has committed identity fraud, the services and products a consumer is most likely to purchase, the success of marketing techniques, and the credit risk associated with certain mortgage loan applications. These scores can influence whether a transaction requires further scrutiny, and whether companies make special offers to certain consumers.
Panelists identified consumer benefits to the use of predictive scores. Alternative scoring protects consumers by alerting fraudulent transactions. Predictive analytics also facilitate relevant marketing. The goal of predictive modeling is to target consumers with personalized and relevant offers. As part of the seminar, Independent Researcher Ashkan Soltani presented on emerging trends in online pricing. Soltani explained that companies often use predictive data, including single data points such as user agent (i.e. using a Mac versus a PC), and zip code, to determine the price and credit offerings to show consumers visiting their websites. According to Soltani’s research, credit card companies market certain credit card products to certain consumers based on predictive data about the consumer’s behavior and digital profile. The relevancy of the offer hinges on the accuracy of the predictive data.
However, according to the FTC, consumers may be unaware that alternative scores exist, and have little to no access to the underlying data that comprise these scores. There is no requirement that consumers have access to alternative scores that are not used for eligibility purposes under the Fair Credit Reporting Act (“FCRA”). Data brokers can generate and sell reports that bear on credit worthiness; but as long as the reports are not used to determine eligibility, these reports are not covered by FCRA. Consumer activists argued that, while predictive scores are not used to determine eligibility in credit decision-making or underwriting, they are used to determine which consumers receive certain offers of credit, housing, employment, and discounts for products and services. In light of this, consumer advocates asserted that tailored offers, based on concealed and potentially inaccurate data, disparately impact vulnerable communities because these communities may assume that the products they are offered are the only products available. They contend that these score are just as impactful as FCRA-regulated scores and should be addressed.
Panelists concluded the seminar by considering ways to protect against potentially disparate effects and improve consumer awareness. Three primary themes emerged from the panel’s discussion:
- Transparency. Consumer advocates called for greater transparency in the collection and use of predictive data. Particularly, consumers should know the scores that exist, the factors they measure, and the accuracy of the underlying data.
- Enforcement of Current Regulations. Other panelists suggested that the FTC focus on enforcing existing laws. Marketing industry representatives cited the FTC’s enforcement action against Spokeo, Inc. as evidence that current regulations are comprehensive. Additionally, panelists pointed to the FTC’s privacy framework and Section 5 of the FTC Act as existing tools the FTC may use to prevent and prohibit unfair or deceptive offers.
- Robust Self-Regulation. Finally, marketing industry representatives noted that companies have strong incentives to self-regulate and create mechanisms to protect consumer privacy.