A benchmark study on methods to ensure fair algorithmic decisions for credit scoring. (arXiv:2209.07912v1 [cs.LG])

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 …

arxiv benchmark credit decisions fair scoring study