A benchmark study on methods to ensure fair algorithmic decisions for credit scoring

The utility of machine learning in evaluating the creditworthiness of loan applicants has been proofed since decades ago. However, automatic decisions may lead to different treatments over groups or individuals, potentially causing discrimination. This paper benchmarks 12 top bias mitigation methods discussing their performance based on 5 different fairness metrics, accuracy achieved and potential profits for the financial institutions. Our findings show the difficulties in achieving fairness while preserving accuracy and profits. Additionally, it highlights some of the best and worst performers and helps bridging the gap between experimental machine learning and its industrial application.