The Fairness of Credit Scoring Models
This addresses fairness issues in credit markets for lenders, regulators, and protected groups, but it is incremental as it builds on existing fairness testing and optimization methods.
The paper tackles the problem of unintentional discrimination in credit scoring models by developing a framework to formally test algorithmic fairness, identify responsible variables, and optimize the fairness-performance trade-off, resulting in guidance for maintaining high forecasting accuracy while improving fairness for protected groups.
In credit markets, screening algorithms aim to discriminate between good-type and bad-type borrowers. However, when doing so, they can also discriminate between individuals sharing a protected attribute (e.g. gender, age, racial origin) and the rest of the population. This can be unintentional and originate from the training dataset or from the model itself. We show how to formally test the algorithmic fairness of scoring models and how to identify the variables responsible for any lack of fairness. We then use these variables to optimize the fairness-performance trade-off. Our framework provides guidance on how algorithmic fairness can be monitored by lenders, controlled by their regulators, improved for the benefit of protected groups, while still maintaining a high level of forecasting accuracy.