Financial risk prediction method and device based on automatic preferred modeling and electronic equipment

Abstract

The invention provides a financial risk prediction method and device based on automatic preferred modeling and electronic equipment. The method comprises the following steps: presetting a model configuration file, wherein the model configuration file comprises a model generation file for automatically generating each model; receiving user input, wherein the user input comprises financial product time nodes and model generation parameters; performing matching processing, anddetermininga model generation file matched with the user input ; generating a financial risk model set according to the matched model generation file, and automatically training each financial risk model; selecting a corresponding test data set, performing effect evaluation on each model in the financial risk model set,and automatically selecting an optimal financial risk model according to evaluation indexes; and predicting the financial risk of the new user by using the optimal financial risk model. According to the invention, the model can be generated more automatically, the working efficiency of business personnel is improved, and the modeling system is optimized.

Description

technical field [0001] The invention relates to the field of computer information processing, in particular to a financial risk prediction method, device and electronic equipment based on automatic optimal modeling. Background technique [0002] Risk forecasting is the quantification of risk and a key technology of risk management. At present, risk prediction is generally carried out through modeling. In the process of model establishment, there are mainly steps such as data extraction, feature generation, feature selection, algorithm model generation, and rationality assessment. [0003] In the prior art, the main purpose of financial risk prediction is how to distinguish good customers from bad customers, evaluate the risk situation of users, so as to reduce credit risk and realize profit maximization. In addition, as the sources of data become more and more abundant, more and more data can be used as risk characteristic variables. However, many data, such as user data a...

AI Technical Summary

Problems solved by technology

However, many data, such as user data and other related data, do not consider changes caused by time factors when using them. Therefore, when using the above data for model calculations, the model calculation values ​​are not accurate enough, and even the risk assessment of some users is less accurate

In addition, relevant business personnel are conducting business analysis, and performing business tasks such as feature engineering, model development, evaluation, online, and monitoring, which usually require relevant business personnel to spend a lot of time to achieve

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