Enhancing Fairness in Credit Assessment: Mitigation Strategies and Implementation

Abstract

Credit assessment remains crucial in determining an individual’s creditworthiness, significantly influencing their financial opportunities. However, traditional credit assessment models have raised concerns about fairness and potential biases, which requires a rigorous evaluation and implementation of fairness in these models. This paper explores the assessment and integration of fairness in credit assessment. It explores various bias mitigation techniques applied across three critical stages: 1) pre-processing; 2) in-processing; and 3) post-processing. By systematically evaluating these techniques, the study demonstrates their effectiveness in reducing disparities and enhancing fairness to varying degrees. The findings underscore the importance of addressing age-based bias in credit assessment predictions through multiple techniques. However, a nuanced analysis is imperative to understand the trade-offs between predictive accuracy and fairness. This critical analysis not only contributes to advancing the theoretical foundations of credit assessment, but also provides practical insights for policymakers and financial institutions striving to adopt more equitable credit evaluation frameworks. Therefore, this paper contributes to the ongoing discourse on enhancing the fairness of credit assessment models, offering actionable recommendations to improve both accuracy and fairness in decision-making processes. By exploring and evaluating a spectrum of bias mitigation strategies, this paper fosters a more inclusive financial landscape where credit decisions are both transparent and accountable.

Keywords

• Credit assessment
• fairness
• bias mitigation
• bias metrics
• protected attributes
• machine learning