Fintech Legal Report – July 2022 | Perkins Coie

[co-author: Dania Assas]

Weekly Fintech focus

• The CFPB ends a no-action letter with an AI credit insurer.
• A circular from the CFPB confirms that AI insurance models are subject to anti-discrimination laws, including adverse action notifications.
• BNPL companies and credit bureaus face calls from the CFPB to properly report consumer information.
• The CFPB is launching an initiative to improve customer service at major banks.

CFPB Ends AI Credit Underwriting No-action Letter

On June 8, 2022, the Consumer Financial Protection Bureau (CFPB) issued an order terminating a no-action letter (NAL) that the CFPB had originally issued to lending platform Upstart Network, Inc. (Upstart), in 2017 (the CFPB’s first-ever NAL) and subsequently renewed in November 2020 for a three-year period.

Under the CFPB’s NAL policy, a person may apply for no-action treatment of a new product or service that offers the potential for significant consumer benefit where there is uncertainty about how the CFPB will apply specific statutory provisions. A grant of no-action treatment means that the CFPB has no current intention to take any supervisory or enforcement action against the recipient with respect to the subject of the NAL.

The CFPB’s termination of the Upstart NAL is the latest in a series of actions by the CFPB that have raised questions about the future of the NAL policy. The following timeline briefly summarizes these events:

• In 2017, Upstart requested an NAL from the CFPB to clarify that Upstart’s credit underwriting model, which involved proprietary applications of artificial intelligence and machine learning to supplement traditional credit scoring methodologies, did not constitute a violation of the Equal Credit Opportunity Act (ECOA) and Regulation B. The CFPB granted Upstart’s request , making Upstart the first entity to receive no-action treatment under the CFPB’s then-new NAL policy.
• On November 30, 2020, the CFPB renewed Upstart’s NAL for an additional three years. The terms and conditions of the NAL required Upstart to notify the CFPB of significant changes to Upstart’s model.
• On April 13, 2022, Upstart notified the CFPB that it intended to add new variables to its underwriting and pricing model. According to the CFPB’s June 8 termination order, CFPB staff requested more time to review Upstart’s proposal.
• On May 24, 2022, the CFPB announced that it was replacing its Office of Innovation (which processed the applications for NALs) and its Project Catalyst (another initiative designed to encourage innovation) with a new Office of Competition and Innovation. The CFPB’s press release stated that “[a]After a review of these programs, the agency concludes that the initiatives proved ineffective and that some firms participating in these programs made public statements indicating that the Bureau had given them benefits that the Bureau expressly did not.”
• On May 27, 2022, pursuant to the CFPB’s termination order, Upstart asked the CFPB to amend the NAL to reduce its term from 36 months to 18 months, meaning it would terminate three days later on May 30, 2022.
• On June 8, 2022, the CFPB announced that it had issued orders terminating Upstart’s inclusion on the list of approved NALs.

A circular from the CFPB confirms that anti-discrimination laws apply to algorithms

The CFPB has issued a circular confirming that federal anti-discrimination law requires explanations of specific reasons for denying credit applications or taking other adverse actions against applicants. The circular warns companies that use algorithmic decision engines (or AI engines) that a “black-box model” for lending decisions does not exempt the company from explaining adverse actions to applicants as required by law. The agency warns that with some black-box models, users and developers may not be able to understand the reasoning behind the model’s results, which could result in companies failing to meet ECOA’s adverse action notification requirements. ECOA and Regulation B require a creditor to provide notice when it takes an adverse action against an applicant, explaining with specific and precise reasons why the creditor took such action. If the creditor uses technology that does not enable the creditor to explain its decision-making process, the creditor will not be able to comply with the law. In short, complexity, opacity or time in the market will not be considered excuses for failure to meet a creditor’s adverse action notice requirements.

BNPL companies urged by CFPB to report credit data

On June 15, 2022, the CFPB published a blog post following up on its inquiry (which we discussed here) about buy-now-pay-later (BNPL) companies. In the post, the agency encourages BNPL companies to report positive and negative data to credit bureaus when BNPL payments are delivered. Furthermore, the CFPB encourages the BNPL industry to develop standardized BNPL furnishing codes and formats to provide data that fits with the unique BNPL product offering. Although the major credit bureaus have announced plans to accept BNPL data, the CFPB is concerned that the differences between the credit bureaus’ plans will result in inconsistent processing of such data, meaning that submitting such data will have less benefit to consumers. The CFPB will monitor the BNPL industry’s progress as changes to the reporting of consumer credit data are implemented.

CFPB launches initiative to improve customer service at major banks

CFPB Director Rohit Chopra led a town hall meeting on June 14, 2022 in Great Falls, Montana to discuss the agency’s new initiative. The CFPB is asking for comments from consumers about their relationships with their banks, including how they are asserting their rights to better service with major banks and credit institutions. The town hall included local community organizations, advocates, leaders and members of the public. Together, the group discussed the challenges facing rural Montanans and how bank deserts negatively impact Montana’s economic landscape.

Chopra noted that recent banking consolidation has had mixed results for consumers and customer service experiences, particularly in rural communities. Rural customers faced reduced access to banking services as they were more likely to visit smaller banks or credit unions, but now live in unbanked rural banking deserts.

In addition, many financial institutions and technology companies are shifting toward what Chopra calls “algorithmic banking,” which relies on using vast amounts of data about a person through tracking and monitoring to make predictions about their behavior and banking habits. Chopra accepts that the shift away from traditional relationship banking could eliminate discrimination based on human judgement, but warns that automated technologies also pose a concern as algorithmic bias can unfairly influence results.

To revive relationship banking, the CFPB issued a request for information to find out how people can assert their rights to better customer service at their depository institution. “Customers of major banks should not have to run through an obstacle course to get a straight answer about their account,” CEO Chopra said during the town hall meeting. In particular, the CFPB is looking to understand, among other things, (i) the type of information people request from their bank and how they use that information; (ii) what information is not currently available to consumers from their banks; and (iii) any customer service obstacles that inhibit a consumer’s ability to tap (eg, wait times, disconnected calls, or the quality of answers to questions).

The CFPB also seeks to guarantee that algorithmic banking does not receive preferential treatment and must follow the same laws as traditional banks. The CFPB published a policy in March confirming that financial companies must explain to applicants the specific reasons for denying a credit application or taking other negative action. It has also ordered several Big Tech companies, such as Facebook, Apple and Google, to provide the CFPB with information about their efforts to gain more control over payment systems and how they plan to use customer data to feed their algorithms.

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