Product risk early warning method and device, computer device and storage medium

Abstract

The present application relates to the field of artificial intelligence and can be applied to the financial industry, and provides a product risk early warning method and device, a computer device anda storage medium. The methods includes: obtaining product information to be analyzed, carrying out data portrait processing on the product information to be analyzed, obtaining vector data corresponding to product information to be analyzed, the vector data is combined with a plurality of sample vector data of the preset twin neural network model respectively, and the obtained pairs of combined data are input into the preset twin neural network model, the risk probability of the product to be analyzed is obtained, and the risk warning information of the product to be analyzed is pushed according to the risk probability. As that data portrait proces is carried out, The data of the products to be analyzed can be deeply mined, the vector data of the products to be analyzed and the sample vector data can be combined as the input data, and the preset twin neural network model is used to evaluate the similarity of the two input data, so that the risk analysis results and the corresponding warning information can be more accurate and effective.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, in particular to a product risk early warning method, device, computer equipment and storage medium. Background technique [0002] With the development of the financial industry, more and more financial investment products similar to bonds have emerged. For example, major enterprises such as central enterprises, state-owned enterprises, and private enterprises can also issue bonds and other products. When investors choose investment products, In order to protect their own interests and avoid property losses, they generally tend to invest in less risky investment products. [0003] Traditional risk analysis methods for investment products need to use a large amount of historical data for evaluation to determine their potential risks. However, in actual situations, the proportion of negative samples that have defaulted in bond data is extremely small. Traditional methods ...

AI Technical Summary

Problems solved by technology

[0003] Traditional risk analysis methods for investment products need to use a large amount of historical data for evaluation to determine their potential risks. However, in actual situations, the proportion of negative samples that have defaulted in bond data is extremely small. Traditional methods are difficult to achieve effective risk analysis for products

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