Prediction and Analysis of Financial Default Loan Behavior Based on Machine Learning Model

Abstract

In recent years, the increase of customer loan risk and the aggravation of the epidemic have led to the increase of customer default risk. Therefore, identifying high-risk customers has become an important research hotspot for banks. The customer's credit is the standard to evaluate the loan amount and interest rate, and the ability to quickly identify customer information has become a research hotspot. Based on the bank credit application scenario, this paper realizes function extraction and data processing for customer basic attribute data and download transaction data. Then, a linear regression model with penalty and a neural network prediction model are proposed to improve the accuracy of bankruptcy assessment and achieve local optimization. In this way, the implicit risk prediction and control of customer credit are improved, and the default risk of bank loans is significantly reduced. According to the characteristics of the collected sample data, the most appropriate penalty linear regression prediction algorithm is selected and the experimental analysis is carried out to improve the risk management level of banks. The experimental results show that the improved logistic regression and neural network model has obvious advantages in the prediction effect for four models.