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CFPB guidance on credit denials by lenders using AI

The Consumer Financial Protection Bureau yesterday announced its issuance of guidance about certain legal requirements that lenders must adhere to when using artificial intelligence and other complex models. The guidance describes how lenders must use specific and accurate reasons when taking adverse actions against consumers. This means that creditors cannot simply use CFPB sample adverse action forms and checklists if they do not reflect the actual reason for the denial of credit or a change of credit conditions. This requirement is especially important with the growth of advanced algorithms and personal consumer data in credit underwriting. Explaining the reasons for adverse actions help improve consumers’ chances for future credit, and protect consumers from illegal discrimination.

The CFPB confirmed in Consumer Financial Protection Circular 2022-03, published at 87 FR 35864 in the June 14, 2022, Federal Register, that the Equal Credit Opportunity Act requires creditors to explain the specific reasons for taking adverse actions. This requirement remains even if those companies use complex algorithms and black-box credit models that make it difficult to identify those reasons. Yesterday’s guidance expands on last year’s circular by explaining that sample adverse action checklists should not be considered exhaustive, nor do they automatically cover a creditor’s legal requirements.

Specifically, Tuesday's guidance explains that even for adverse decisions made by complex algorithms, creditors must provide accurate and specific reasons. Generally, creditors cannot state the reasons for adverse actions by pointing to a broad bucket. For instance, if a creditor decides to lower the limit on a consumer’s credit line based on behavioral spending data, the explanation would likely need to provide more details about the specific negative behaviors that led to the reduction beyond a general reason like “purchasing history.” Creditors must disclose the specific reasons, even if consumers may be surprised, upset, or angered to learn their credit applications were being graded on data that may not intuitively relate to their finances.

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