ALGORITHMICSOLUTIONSFORFAIRCRE DITSCORING

This paper explores algorithmic decision-making methods to enhance fairness in credit scoring systems, addressing concerns of bias and discrimination. Through the application of machine learning techniques and fairness-aware algorithms, the proposed methods aim to mitigate disparities in credit assessment based on demographic factors such as race, gender, or socioeconomic status. By incorporating fairness constraints into the model training process, these methods strive to achieve equitable outcomes while maintaining predictive accuracy and regulatory compliance. Through extensive experimentation and evaluation on real-world credit datasets, the effectiveness and fairness of the proposed algorithms are demonstrated, highlighting their potential to improve access to credit and financial inclusion for historically marginalized groups.