Financial service risk prediction method and device

Abstract

The embodiment of the invention provides a financial service risk prediction method and device. The method comprises the steps of receiving a financial service risk prediction request for a target user; selecting one of the plurality of financial service risk prediction models as a target financial service risk prediction model based on the financial service request type, wherein the financial service risk prediction model is obtained by training by using respective corresponding training sets, and the training sets are target data sets obtained by performing data binning on historical user financial information of multiple users by using a Spark system; and inputting the user financial information into the target financial service risk prediction model, and taking output as a financial service risk prediction result of the target user. The reliability, efficiency and automation degree of the binning process of the training data can be effectively improved, and then the accuracy, efficiency and automation degree of financial service risk prediction of financial institution users by applying the financial service risk prediction model obtained by binning data training can be effectively improved.

Description

technical field [0001] This application relates to the technical field of data processing, in particular to a financial service risk prediction method and device. Background technique [0002] As financial institutions such as banks provide more and more types of financial services to the public, and financial services target more and more target groups, financial institutions need to provide financial services to users before providing certain financial services. Pre-judgment of possible risks in the service. At present, the way financial institutions conduct financial service risk prediction usually uses machine learning models for automatic prediction, but the machine learning models currently used by financial institutions usually require a large amount of historical data for training, and it takes a lot of time to apply these historical data. manpower for data mining and processing. However, the method of data processing by manpower is time-consuming and labor-intensi...

AI Technical Summary

Problems solved by technology

However, due to the problem that the unsupervised binning method does not consider the dependent variable, it will limit the improvement of the effect of the trained machine learning model to a certain extent, and the trained machine learning model is unstable for unevenly distributed data sets. Manual intervention is required, so the accuracy and automation of financial service risk prediction cannot be guaranteed, and the calculation process of the supervised binning method is more complicated, time-consuming and labor-intensive

That is to say, the existing financial service risk forecasting process cannot satisfy the accuracy and efficiency of financial service risk forecasting at the same time.

Images