Research on Personal Loan Default Risk Assessment Based on Machine Learning - Liu Guangsen (ITM Web of Conferences)
In the present era of rapid development of the Internet and big data, the scale of personal loans and the complexity of personal credit data are growing rapidly. Accurately assessing personal credit rating and personal loan default risk has become an important topic in the financial field. This paper analyzes the current research status of other scholars on machine learning in personal loan default risk assessment in recent years, and selects Logistic Regression, Support Vector Machine, Naïve Bayes and Deep Neural Networks as research model. Meanwhile, this paper selects the Kaggle website data of a bank and credit information bureau in India, preprocesses the dataset and applies it to the training and testing of the models, and finally derives the performance results of the four models. The results of the study show that the machine learning models have better accuracy and higher efficiency in analyzing personal credit data and assessing the risk of personal loan default. Among them, the Deep Neural Network has the best overall performance compared to the other three machine learning models. The research in this paper has certain research significance for the research of machine learning in personal loan default risk assessment.