When Culture Becomes A Credit Risk: What Lenders Are Getting Wrong

Abstract

Credit decisions at lending institutions are expected to reflect a borrower’s creditworthiness and ability to repay. Yet, credit officers routinely and subconsciously allow cultural biases based on caste, religion, ethnicity, or community to contaminate their loan decisions. Biased credit decisions can result in loan approvals to borrowers with poorer credit outcomes, rejection of creditworthy applicants, degraded loan performance, and lost returns. A Study by Francesco D’Acunto, Pulak Ghosh and Alberto G. Rossi shows that data-driven recommendations can reduce such biases and move lenders away from borrowers they may otherwise favour because of shared cultural identity. For credit risk leaders and lending managers, this implies that algorithmic recommendations should be used alongside human judgment to mitigate cultural bias and improve lending decisions