The Consumer Financial Protection Bureau recently issued guidance about the legal requirements that lenders must adhere to when using artificial intelligence and other complex models.

The guidance sheds light on the kind of specificity that regulators are looks for in profiling disclosures. It also can inform companies on how to drafting privacy notices.

The CFPB has made the intersection of fair lending and technology a priority.

“Creditors must be able to specifically explain their reasons for denial. There is no special exemption for artificial intelligence,” CFPB Director Rohit Chopra said in a statement.

Some key points:

• Lenders must use specific and accurate reasons when taking adverse actions against consumers. This requirement remains even if those companies use complex algorithms and black-box credit models that make it difficult to identify those reasons.
• 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.
• It is the duty of the creditor — if it chooses to use the sample forms — to either modify the form or check “other” and include the appropriate explanation.
• Explaining the reasons for adverse actions help improve consumers’ chances for future credit, and protect consumers from illegal discrimination.
• Creditors will not be in compliance with the law by disclosing reasons that are overly broad or vague
• 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, such as the type of establishment at which a consumer shops or the type of goods purchased, 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. For instance, creditors sometimes considers data that is not typically found in a consumer’s credit file or credit application.

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