An Analytical Framework for Bias Mitigation in Credit Scoring Systems through Fairness-Constrained Neural Optimization

Machine learning has significantly enhanced predictive accuracy in credit scoring systems; however, it has also intensified concerns regarding algorithmic bias and fairness. This paper introduces an analytical framework that integrates fairness constraints into neural network optimization to mitigate such biases. We propose a constrained optimization methodology based on Lagrangian relaxation and fairness-aware loss functions to align predictive performance with equity objectives. Using a real-world credit dataset, we demonstrate that the proposed framework effectively reduces disparate impact across sensitive attributes such as race and gender while maintaining predictive performance. Additionally, the model incorporates group fairness constraintssuch as demographic parity and equal opportunitydirectly into the neural network’s loss function. Empirical evaluations show that our method consistently outperforms baseline models in terms of both fairness metrics and classification accuracy. This study offers a systematic approach to ethically aligning financial decision-making algorithms with broader societal fairness imperatives