Algorithmic decision making methods for fair credit scoring
It addresses fairness issues in automated credit decisions for financial institutions, but is incremental as it evaluates existing methods rather than proposing new ones.
This paper tackled the problem of algorithmic bias in credit scoring by evaluating 12 bias mitigation methods across 5 fairness metrics, finding challenges in balancing fairness with accuracy and profitability, and identifying the most and least successful methods.
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.