Check Your Bias! A Field Guide for Lenders

Artificial intelligence (AI) and machine learning (ML) have opened up the possibility of scoring new and alternative data sources, either to complement or to replace more traditional lending methodologies. But how do financial services providers (FSPs) ensure these new systems are both efficient and fair? Amidst the backdrop of a rapidly changing credit landscape, this practical field guide walks executives and data scientists alike through recommendations for ensuring that revised and new credit scoring methods are not unintentionally excluding women.

This guide combines academic work on bias detection with practical experience analyzing administrative data from real lenders working to increase financial inclusion around the world. The diversity of institutions this report references offered a natural test for generalizability of a core set of easy-to-understand bias detection questions. Although the focus of the study is on detecting gender biases, the same tools and principles can be applied to bias detection for any underrepresented group.