基于机器学习的网贷借款人违约预测研究

Research on Default Prediction of Online Lending Borrowers Based on Machine Learning

摘要: 网络借贷行业的飞速发展使得传统风控在数据时效性、全面性和层次性上的短板日益凸显。而目前，机器学习的蓬勃兴起使网贷平台能够利用多维大数据构建智能风控模型，更加准确的评估个人信用状况，有效地降低违约风险。本文基于中诚信征信有限公司提供的借款人贷款风险数据，利用Logistic、XGBoost和NN构建预测模型，并将结果进行对比。由于XGBoost算法具有高度的灵活性，允许自定义优化目标和评价标准，并且参数较多，可调整的范围大，因此基于XGBoost算法构建的模型对网贷借款人违约预测的准确率较高。同时，本文利用自动化调参工具遍历所有参数组合，给模型调参带来了极大的便利。

Abstract: The rapid development of the online lending industry has made traditional risk control increasingly prominent in the timeliness, comprehensiveness and hierarchy of data. At present, the booming machine learning enables the online lending platform to build an intelligent risk control model by using multi-dimensional big data so that it can assess personal credit status more accurately and reduce default risk more effectively. Based on the borrower loan risk data provided by CCX Credit Technology, this paper uses Logistic, XGBoost and NN to construct a forecasting model and compares the results. The XGBoost algorithm has a high degree of flexibility and allows custom optimization goals and evaluation criteria, and it also has more parameters, the scope of adjustment is large. So the model built based on XGBoost algorithm has higher accuracy for default prediction of online loan borrower. At the same time, this article uses the automated tuning tool to traverse all the parameter combinations, which brings great convenience to the model tuning.