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.