Time to Assess Bias in Machine Learning Models for Credit Decisions

Focus on fair lending has become more intensified recently as bank and non-bank lenders apply artificial-intelligence (AI)-based credit determination approaches. The data analytics technique behind AI and machine learning (ML) has proven to be powerful in many application areas. However, ML can be less transparent and explainable than traditional regression models, which may raise unique questions about its compliance with fair lending laws. ML may also reduce potential for discrimination, by reducing discretionary and judgmental decisions. As financial institutions continue to explore ML applications in loan underwriting and pricing, the fair lending assessments typically led by compliance and legal functions will likely continue to evolve. In this paper, the author discusses unique considerations around ML in the existing fair lending risk assessment practice for underwriting and pricing models and proposes consideration of additional evaluations to be added in the present practice.

Automated summary

Focus on fair lending has increased recently as bank and non-bank lenders adopt AI-based credit decision methods. However, the transparency and explainability of AI and ML techniques may raise unique concerns about compliance with fair lending laws compared to traditional regression models. Nonetheless, ML technology can reduce discretionary and subjective decisions, thus reducing the potential for discrimination. Therefore, as financial institutions explore ML applications in loan underwriting and pricing, fair lending assessments will continue to evolve and require additional evaluations.