Research on prediction of credit score classification data based on machine learning methods

Abstract

Credit scoring is an important tool for financial institutions to assess customer credit risk, and its accuracy directly affects the effectiveness of risk management and decision-making. With the development of big data and artificial intelligence technology, the application of machine learning methods in the field of credit scoring has gradually become a research hotspot. This study explores the application of machine learning methods in credit scoring, specifically the use of random forest models to analyze real credit scoring data. By conducting feature importance analysis on public data sets from Kaggle, and combining statistical methods such as AUC (area under the curve) and Kolmogorov-Smirnov values to evaluate model performance, the study found that the random forest model performed well when processing complex and high-dimensional data, significantly It improves the prediction accuracy. Feature analysis reveals the key impact of factors such as income level, credit history length, and debt ratio on credit scores. This paper deepens the theoretical understanding of the application of machine learning in the field of credit scoring, which provides financial institutions with more reliable tools in risk management and decision-making. However, there are still limitations: the limited geographical and industry coverage of the dataset may affect the generalizability of the results, and the high computational resource requirements of the ensemble learning method limit its promotion in real-time applications.