Credit Grade Prediction Based on Decision Tree Model
Jiexian You, LI Guo-lan, Hongjun Wang · 2021 16th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) · 2021
With the rapid development of China’s economy, credit business has become one of the main business of financial institutions such as banks, and the evaluation of personal credit is one of the important components of loan business of financial institutions such as banks. However, how to accurately and efficiently predict the credit of borrowers has become an urgent problem for practitioners in the financial field. More and more attention has been paid to using machine learning algorithm to predict people’s credit rating in the era of artificial intelligence. Using previous data, this paper uses five models of support vector machine, K-nearest neighbor, naive bayes, convolutional neural network and decision tree in machine learning algorithms to predict people’s credit rating in four countries of China, Japan, Australia and Germany by modeling, using accuracy and F1score as evaluation criteria, the accuracy of five models for credit prediction is obtained, and the definition and structure of decision tree model, principle, and decision tree construction and algorithm are emphatically introduced. The results show that the accuracy of decision tree algorithm models for credit prediction is relatively high and the most stable.