基于模型融合的互联网信贷信用风险预测研究
Research on Internet Credit Risk Prediction Based on Model Fusion

互联网信贷信用风险的预测是互联网金融可持续发展的关键因素，在放贷前准确预估借款人的信用风险，能有效较低企业可能的风险损失。随着机器学习的发展，机器学习的算法模型在互联网信贷信用风险方面的应用也越来越多。为了探究树模型和线性模型融合在互联网信贷信用风险预测的效果，本文采用Stacking模型融合方法设计了信用风险预测模型，其中第一层模型为随机森林、XGBoost、LightGBM，第二层模型为逻辑回归。并且在拍拍贷的真实数据上进行实验，对比了融合后的模型和单模型在AUC、准确率和耗时上的表现，结果表明融合后的模型虽然耗时长一些，但是在AUC和准确率方面都比单模型的效果要好，为互联网金融信贷风险预测模型的构建提供了一个新的思路。
The prediction of the credit risk of Internet credit is a key factor for the sustainable development of Internet finance. It can accurately estimate the credit risk of borrowers before lending, effectively reducing the possible risk loss of enterprises. With the development of machine learning, the algorithm model of machine learning has been applied more and more in the credit risk of Internet credit. In order to explore the effect of integrating tree model and linear model in the prediction of credit risk of Internet credit, this paper adopts Stacking model fusion method to design the credit risk prediction model, in which the first layer model is random forest, XGBoost and LightGBM and the second layer model is logistic regression, and conducts experiments on the real data of Clap to Borrow. Compared with the performance of the single model on AUC, accuracy and time consuming, the results show that the fused model, although takes longer time, but performs better in terms of AUC and accuracy, which provides a new idea for the construction of financial credit risk prediction model.