Towards Fair Credit Risk Assessment: Developing Mathematical Models with Equity and Accuracy

Credit default risk is an important factor in determining whether a person can apply for a credit card and continue to use it. Hothis studyver, the assessment of credit default risk should be fair and unbiased, especially as credit cards become an increasingly popular payment method. In this paper, this study analyze how to assess a user’s credit default risk while emphasizing the importance of machine learning fairness. this study propose using principal component analysis (PCA) to extract key factors for judging credit default risk and a BP neural network to evaluate and analyze these factors. this study also propose adversarial representation learning which is aim to address discrimination against different minority group people. Our main purpose is to train the main network to generate features that the discriminator cannot use to accurately predict the sensitive attribute. By doing so, the learned features become fairer and do not contain discriminatory information. Therefore, adversarial representation learning is aimed at reducing discrimination against minority groups in machine learning models. Our approach serves as a natural method for ensuring that these parties act fairly and various adversarial objectives. this study demonstrate that selecting the appropriate objective is essential for achieving fair prediction. Through our approach, this study aim to ensure that our credit default risk assessment is fair and equitable for all users of different gender, race, and education.