Prediction of Credit Card Loan Risk Based on Multilayer Perceptron Neural Network Model

Abstract

Under the background of Internet finance and the popularization of intelligent terminal equipment, the scale of data and the diversity of information have developed exponentially rapidly, while the bad debt rate of personal credit is vague and much higher than that of commercial banks in the traditional technology period. Most of the traditional credit evaluation is driven by models, and the robustness of the risk control system is poor, which cannot meet the increasingly complex demand for default risk prediction. Since commercial banks have accumulated large-scale data assets, it has great significance to combine machine learning technology to help banks extract effective information from massive data and achieve risk assessment of borrowers, thereby reducing the risk of default in borrowing. This paper selects a credit card related data set, uses principal component analysis to extract feature vectors, and obtains a risk assessment index system for borrowers. Then, based on MLP neural network, a credit card loan risk prediction model is constructed. The study reads: The MLP model based on principal component analysis has high accuracy, fast running speed and strong stability. It is an ideal model for commercial banks to evaluate the credit risk of borrowers. This study can provide a new reference for commercial banks to solve the problem of credit risk prediction.