Research on credit scoring model optimization based on machine learning

With the rapid advancement of financial technology, traditional credit scoring methods are becoming increasingly incapable of dealing with complex data. This study is dedicated to using machine learning technology to optimize the credit scoring model in order to enhance its predictive performance and generalization ability. This study constructs an integrated learning model that integrates three algorithms: random forest, support vector machine, and gradient boosting machine, and uses a dynamic feature update mechanism and advanced outlier processing strategies to achieve model optimization. Studies have shown that the proposed model has significantly improved accuracy, recall rate, and AUC value in predicting credit risk. Studies have shown that integrated learning technology has significantly improved the stability and accuracy of the credit scoring model. At the same time, the continuous refreshing of dynamic features and the precise processing of outliers have significantly enhanced the model’s ability to adapt to changes and its reliability.