Loan Approval Prediction Improved by XGBoost Model Based on Four-Vector Optimization Algorithm

Abstract

Abstract. This paper discusses an improved XGBoost model based on four-vector optimization algorithm to improve the accuracy of loan approval prediction. Through the analysis of correlation heat maps, we found that there were significant positive and negative correlations among some variables, which laid the foundation for subsequent machine learning analysis. Based on this, we compare the traditional machine learning algorithm with the improved model to evaluate its performance in loan approval forecasting. In the confusion matrix analysis of the training set, the improved XGBoost model demonstrated excellent performance, with all loan approval predictions being correct with 100% accuracy. However, the performance in the test set was slightly different, with 1,182 projects receiving correct loan approval predictions and 99 projects forecasting errors. Of these, 26 projects that should have been predicted to be "unapproved" were incorrectly labeled as "approved," while 73 projects that should have been predicted to be "approved" were incorrectly labeled as "unapproved." These results suggest that we still need to pay attention to the misjudgment of the model in practical application. By synthesizing all model evaluation indicators, we found that the improved XGBoost model based on the four-vector optimization algorithm has a higher accuracy in loan approval prediction than the traditional XGBoost model, with an increase of 1.5%. In addition, the other evaluation indicators also show a trend of significantly better than the traditional model. This study shows that the four-vector optimization algorithm can effectively improve the application effect of XGBoost model in the field of loan approval, and provide more accurate data support and decision-making basis for the financial industry. In the future, we will continue to explore the potential and application prospects of this algorithm in other fields.