Fintech Legal Report – July 2022 | Coie Perkins

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[co-author: Dania Assas]

Weekly Fintech focus

  • The CFPB is terminating a no-action letter with an AI credit underwriter.
  • A CFPB circular confirms that IA’s underwriting models are subject to anti-discrimination laws, including adverse action notices.
  • BNPL companies and credit bureaus are urged by the CFPB to properly report consumer information.
  • The CFPB is launching an initiative to improve customer service at major banks.

CFPB Terminates AI Credit Underwriting No Action Letter

On June 8, 2022, the Consumer Financial Protection Bureau (CFPB) issued a order ending a No Action Letter (NAL) that the CFPB originally granted to lending platform Upstart Network, Inc. (Upstart), in 2017 (CFPB’s first-ever NAL) and subsequently renewed in November 2020 for a period of three years.

Under the CFPB’s NAL policy, a person may seek treatment without action on a new product or service that offers the potential for significant consumer benefits where there is uncertainty as to how the CFPB would apply provisions specific to the law. Granting treatment without action means that the CFPB does not currently intend to take supervisory or enforcement action against the recipient with respect to the subject matter of the NAL.

The CFPB’s termination of Upstart NAL is the latest in a series of CFPB actions that have raised questions about the future of its 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 complement traditional credit scoring methodologies, did not present 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 new policy NAL of the CFPB.
  • On November 30, 2020, the CFPB renewed NAL from Upstart for another three years. The terms and conditions of the NAL required Upstart to notify the CFPB of material changes to Upstart’s model.
  • On April 13, 2022, Upstart notified the CFPB of its intention 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 announcement that it was replacing its Office of Innovation (which handled NAL applications) and its Project Catalyst (another initiative to encourage innovation) with a new Office of Competition and Innovation. The CFPB press release states that “[a]fter a review of these programs, the agency concludes that the initiatives have proven ineffective and that some firms participating in these programs have made public statements that the Bureau has provided them with benefits that the Bureau has not expressly granted.
  • On May 27, 2022, per the CFPB Termination Order, Upstart asked the CFPB to amend the NAL to reduce its term from 36 months to 18 months, meaning it would end three days later on May 30, 2022 .
  • On June 8, 2022, the CFPB announcement that he issued the order to terminate Upstart’s listing on his list of approved NALs.

CFPB Circular Confirms Anti-Discrimination Laws Apply to Algorithms

The CFPB published a circular confirming that federal anti-discrimination law requires explanations of the specific reasons for denying credit applications or taking other adverse action against applicants. The circular warns companies using algorithmic decision engines (or AI engines) that a “black box model” for loan decisions does not exempt the company from explaining adverse actions to applicants as required. the law. The agency warns that with some black box models, users and developers may not be able to know the reasoning behind the model’s results, which could prevent companies from meeting adverse action notification requirements. of ECOA. ECOA and Regulation B require a creditor to provide notice when taking adverse action against a plaintiff, explaining with specific and specific reasons why the creditor took such action. If the creditor uses technology that does not allow them to explain their decision-making process, then 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 notice of adverse action requirements.

BNPL companies invited by the CFPB to declare their credit data

On June 15, 2022, the CFPB published a blog post followed by his request (which we talked about here) into “buy now, pay later” (BNPL) companies. In the message, the agency urges BNPL companies to report positive and negative data to credit bureaus when BNPL payments are provided. In addition, the CFPB encourages the BNPL industry to develop standardized BNPL furnishing codes and formats to provide data that matches BNPL’s unique product offering. Although the major credit bureaus have announced their intention to accept BNPL data, the CFPB is concerned that differences between credit bureau plans could lead to inconsistent treatment of this data, meaning that the provision of this data will benefit consumers less. The CFPB will monitor the BNPL industry’s progress as the consumer credit data reporting changes are implemented.

CFPB launches initiative to improve customer service in major banks

CFPB Director Rohit Chopra led a town hall on June 14, 2022 in Great Falls, Montana to discuss the agency’s new initiative. The CFPB is seeking feedback from consumers on their relationship with their banks, including how they assert 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 faced by people in rural Montana and how banking deserts negatively affect Montana’s financial landscape.

Chopra noted that recent banking consolidation has had mixed results for consumers and customer service experiences, especially in rural communities. Rural customers faced reduced access to banking services as they were more likely to go to smaller banks or credit unions, but now live in rural banking deserts with no intimate banking relationship.

Additionally, many financial institutions and tech companies are turning to what Chopra calls “algorithmic banking,” which relies on using large amounts of data about an individual through tracking and surveillance to make predictions. on his behavior and banking habits. Chopra concedes that moving away from traditional banking relationships could eliminate discrimination based on human judgment, but warns that automated technologies are also a problem, as algorithmic biases can unfairly affect results.

To revitalize relationship banking, the CFPB has launched a Request for Information to find out how everyone can assert their right to better customer service with their depository institution. “Big bank customers shouldn’t have to go through an obstacle course to get a clear answer on their account,” Director Chopra said at the town hall meeting. In particular, the CFPB seeks to understand, among other things, (i) the types 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 impediments that impede a consumer’s ability to bank (for example, wait times, disconnected calls, or the quality of answers to questions).

The CFPB also seeks to ensure that the algorithmic bank does not receive preferential treatment and must follow the same laws as traditional banks. The CFPB issued a policy in March confirming that financial firms must explain to applicants the specific reasons for denying a credit application or taking other adverse action. He also ordered several Big Tech companies, such as Facebook, Apple and Google, to provide the CFPB with information about their efforts to better monitor payment systems and how they plan to use customer data to power their algorithms.

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