Financial risk prediction method and device based on Bayesian deep learning and electronic equipment

Abstract

The invention provides a financial risk prediction method and device based on Bayesian deep learning and electronic equipment. The method comprises the steps of obtaining a historical user data set, and establishing a training data set and a test data set according to the historical user data set; constructing a Bayesian deep learning model, and performing parameter estimation on to-be-optimized parameters of the deep neural network by using a Bayesian statistical method, the to-be-optimized parameters including weight parameters and bias parameters between layers; training the Bayesian deep learning model after parameter optimization by using the training data set; obtaining user feature data of a target user, and calculating a financial risk evaluation value by using a Bayesian deep learning model; and carrying out financial risk prediction on the target user according to a preset judgment rule and the calculated financial risk evaluation value. According to the method, the generalization prediction capability of the model is enhanced, the prediction precision of the model is improved, and financial risks are further reduced.

Description

technical field [0001] The present invention relates to the field of computer information processing, in particular to a financial risk prediction method, device and electronic equipment based on Bayesian deep learning. Background technique [0002] Risk control (referred to as risk control) means that risk managers take various measures and methods to eliminate or reduce the various possibilities of risk events, or risk controllers reduce the losses caused by risk events. Risk control is generally used in the financial industry, such as risk control for company transactions, business transactions or personal transactions. [0003] In the prior art, the main purpose of financial risk assessment is how to distinguish good customers from bad customers, evaluate the risk status of users, and reduce credit risk to maximize profits. At present, the Logistic regression statistical method is mainly used to calculate the risk score. For example, the logistic regression method selec...

AI Technical Summary

Problems solved by technology

At present, the Logistic regression statistical method is mainly used to calculate the risk score. For example, the logistic regression method selects 10-20 features as an introduction, and the effect is not good when dealing with high-dimensional data.

In addition, with the development of machine learning technology, especially the XGBoost model in the tree model has been widely used in the field of financial risk assessment, but this model involves many parameters, and it is difficult to improve the prediction effect after training to a certain extent

Images