Data Mining Of Loan Scenarios In The Field Of Financial Risk Control

Today,machine learning,deep learning have become the cornerstones of the Internet finance industry.They have played a huge role in the field of Internet finance risk control.There are many businesses in the Internet finance industry,the core of which should be the lending business.The lending business not only solves the borrowing needs of users,but also provides investors with significant benefits.The development of lending business in the Internet industry has also made the information in the lending process more transparent and the use of funds more efficient.Through technical means,it can effectively reduce the risk of default in lending,prevent the occurrence of defaults.And it take into account users' capital needs and investment needs,thereby bringing convenience to the people's life and promoting the vigorous development of the Internet finance industry.The financial risk control model has Appeared for it.The financial risk control technology in the lending scene cannot be separated from the support of data,and the data in the Internet is extremely large,how to effectively use the data has become the focus of risk control.This article expands from two data sources.The first is data mining based on the user's mobile phone Application list.In risk control,APP data that is not directly related to financial behavior also has important value,and is often used in anti-fraud,risk control modeling feature engineering,etc.Unlike users 'financial behaviors that can directly reflect the user's loan default risk,mobile Apps can also help users predict the default risk to a certain extent.Therefore,the user's mobile Application list information also has value in the field of risk control.This article will derive variables from the risk control information contained in the mobile phone Application to obtain variables that can predict the risk of loan default.The second is data mining based on users' previous borrowing behavior.Traditional financial risk control models are inseparable from the derivation of risk control features.It takes a lot of time to design derivative variables.At the same time,it takes time to analyze and solve problems when the effect of the model declines.In view of the excellent performance of neural network model on machine learning tasks,this article will use neural network to propose a borrowing risk prediction model based on user's previous borrowing behavior.The model will be directly used on unstructured data,instead of using traditional feature engineering-based risk control modeling methods,thereby improving engineering efficiency to a certain extent.