Application of Decision Tree Model in Personal Credit Scoring and Its Fairness Optimization

Credit scoring is an essential tool for assessing borrower risk in the financial industry. Traditional credit scoring methods often fail to maintain high accuracy and flexibility in the face of complex financial data. In contrast, decision tree modeling is an effective tool for improving the accuracy of credit scoring due to its simplicity and ability to handle complex data. However, decision tree models may also amplify biases in the data, leading to unfair treatment of certain groups in scoring. This paper explores several fairness optimization methods, such as data preprocessing, fairness constraints in model training, and post-processing adjustments to address this issue.It proposes a framework for balancing fairness, accuracy, and efficiency. Although these methods reduce bias to a certain extent, none of them deal with the issue of how to ensure fairness with high precision, so future research should focus on developing optimization algorithms adapted to dynamic environments, balancing multiple objectives, and improving fairness assessment criteria to achieve a fairer and more efficient credit scoring model that will promote the continued development of the financial industry and contribute to the sustainable development of society.