Artificial Intelligence/Machine Learning (AI/ML) models are not new concepts to either the financial industry or regulators. While regulators have been focused on implications of the use of AI/ML for years, they have recently been increasing their scrutiny on the use of such models in credit decisions:
The “Combatting Redlining Initiative” combines the individual agencies’ focus on fair lending in a coordinated enforcement setting. The initiative refers to both depository and non-depository institutions and highlights the role of non-depository lenders in the mortgage space. While this particular initiative focuses on lending activities in the mortgage space, the regulators continue to focus on potential fair-lending violations across a broader range of lending, payment, and servicing activities and asset types such as property assessed clean energy programs, student loans, and small business loans, among others.
Some lenders and servicers have recently undergone or are undergoing fair lending-related examinations, while others are responding to inquiries and investigations, which generally include receiving data requests from the CFPB. Similarly, the CFPB also inquired6 about information from big payment platforms, as well as buy now, pay later providers. These requests were significant in scope, and to the extent that such data is consumed by AI/ML models, there could be regulatory concerns associated with “data harvesting” and data mining in credit decision-making.
Institutions typically translate the information collected during the application process into data, which is then consumed by underwriting and pricing models. However, such data may contain errors and biases against specific consumer groups. Furthermore, institutions may not have clear rules around the data fields consumed by the AI/ML models, which have recently claimed a material presence in model inventories.
In light of the recent regulatory literature, Guidehouse expects that:
Institutions should consider the following actions:
Institutions should focus on providing open access to credit and fair lending/services practices. They should revisit their lending models—including both underwriting and pricing—and evaluate whether models are prone to producing disparate outcomes for disadvantaged consumers/protected classes. Lending institutions that recognize the requirements and expectations of the evolving regulatory landscape are likely to benefit from being proactive and assessing their lending practices with a holistic approach.
Guidehouse offers customized and unique solutions to assist institutions with:
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