Beyond the Algorithm: Ethical Frameworks for Responsible Lending

As Machine Learning (ML) becomes more integrated into financial decision-making, its potential to enable predatory practices that target vulnerable populations raises ethical concerns. The ethical challenges of data-driven subprime auto lending, focusing on research by Valluri, Raju, and Patil (2021) serves as a case example. Their analysis evaluated different ML models designed to reduce customer churn for a Midwestern Credit Union and found that the algorithms promoted predatory lending practices. The authors show how the Integrative Justice Model (IJM) and Asset-Based Community Development (ABCD) frameworks can help Credit Unions adopt ethical lending strategies, reduce churn and remain true to their mission of supporting financially vulnerable individuals. The integration of these frameworks into business education is crucial for fostering responsible decision-making and promoting just lending practices