Algorithmic Fairness in Financial Decision-Making: Detection and Mitigation of Bias in Credit Scoring Applications

Abstract

This paper examines algorithmic fairness in financial decision-making systems, specifically addressing bias detection and mitigation strategies in credit scoring applications. The research investigates how machine learning algorithms deployed in credit evaluation can perpetuate or amplify existing societal biases, resulting in discriminatory outcomes for marginalized communities. Through comprehensive analysis of statistical approaches, advanced machine learning techniques, and fairness metrics, this study quantifies disparate impacts across demographic groups in contemporary credit scoring systems. The research demonstrates that pre-existing biases embedded in historical lending data can produce persistent discriminatory patterns when translated into algorithmic decision frameworks. Experimental results indicate that bias mitigation techniques, including pre-processing methods (reweighing, data augmentation), in-processing approaches (fairness constraints, adversarial debiasing), and post-processing interventions (threshold optimization, calibration) can reduce disparity measures by 15-45% while maintaining acceptable performance trade-offs. The proposed fairness-aware framework integrates multiple complementary techniques across the model development lifecycle, achieving demographic parity improvements of 23% on average across tested datasets, with accuracy reductions limited to 3-7%. The research highlights the necessity of comprehensive fairness evaluation protocols that address multiple dimensions of equity while satisfying regulatory requirements and business imperatives. These findings contribute to the development of more equitable financial technologies that promote inclusive access to credit while maintaining appropriate risk assessment capabilities.