Financial risk analysis method and device based on multilayer model structure and electronic equipment

Abstract

The invention relates to a financial risk analysis method and device based on a multilayer model structure, electronic equipment and a computer readable medium. The financial risk analysis method comprises the following steps: respectively inputting user data into a plurality of initial machine learning models to obtain a plurality of initial analysis values; obtaining at least one conventional variable parameter corresponding to the at least one risk analysis model; inputting the plurality of initial analysis values and the at least one conventional variable parameter into the at least one risk analysis model to generate a risk analysis value; and performing financial risk analysis on the user through the risk analysis value. According to the financial risk analysis method and device based on the multilayer model structure, the electronic equipment and the computer readable medium, financial risk prediction can be carried out on financial users from multiple angles, and transaction risks are prevented, and the financial risk level of the whole user group is optimized.

Description

technical field [0001] The present disclosure relates to the field of computer information processing, in particular, to a financial risk analysis method, device, electronic equipment and computer-readable medium based on a multi-layer model structure. Background technique [0002] With the development of the economy, in order to meet the needs of their own development, individual users or business users often borrow from financial service institutions. For financial service institutions, users' borrowing activities are likely to bring risks to financial service companies. Before the repayment period expires, major adverse changes in the financial and business conditions of the borrower (credit user) are likely to affect its ability to perform, resulting in risks such as bad debts and bad debts. Therefore, in order to reduce the probability of such risks, financial service institutions need Conduct a risk assessment of the borrower. [0003] The financial risks of users are...

AI Technical Summary

Problems solved by technology

In the risk control of transactions, the existing technology often hopes to give safe and reliable risk analysis results as much as possible through a machine learning model, but this approach puts a lot of pressure on the training of the machine learning model and requires multi-dimensional A large amount of user data, and in the training process of the machine learning model, it is difficult to give a comprehensive and accurate user financial risk analysis result with multi-dimensional features

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