Algorithmic Decision Making Methods for Fair Credit Scoring

The effectiveness of machine learning in evaluating the creditworthiness of loan applicants has been demonstrated for a long time. However, there is concern that the use of automated decision-making processes may result in unequal treatment of groups or individuals, potentially leading to discriminatory outcomes. This paper seeks to address this issue by evaluating the effectiveness of 12 leading bias mitigation methods across 5 different fairness metrics, as well as assessing their accuracy and potential profitability for financial institutions. Through our analysis, we have identified the challenges associated with achieving fairness while maintaining accuracy and profitabiliy, and have highlighted both the most successful and least successful mitigation methods. Ultimately, our research serves to bridge the gap between experimental machine learning and its practical applications in the finance industry.

Automated summary

This paper addresses the concern of potential discrimination caused by automated decision-making processes in evaluating the creditworthiness of loan applicants. The effectiveness of 12 bias mitigation methods across fairness metrics, accuracy, and profitability for financial institutions is evaluated. The research identifies challenges in achieving fairness while maintaining accuracy and profitability, and highlights successful and unsuccessful mitigation methods. The study aims to bridge the gap between experimental machine learning and its practical applications in the finance industry.