Business risk prediction method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of big data analysis, in particular to a business risk prediction method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring customer information data and state information data of a service in a plurality of historical time sequences; the client information data and the state information data are mapped to a low-dimensional dense vector space, embedded feature vectors are obtained, and the embedded feature vectors comprise client feature vectors and state feature vectors; performing weighted calculation on the state feature vector based on a multi-head attention mechanism to obtain an associated feature vector, the associated feature vector being used for representing an association relationship between a state of the service in a historical time sequence and a future state; fusing the associated feature vector with the customer feature vector to obtain a fused feature vector; and inputting the fused feature vector into a preset risk classification model for identification to obtain a prediction result of the business risk. By adopting the method, an accurate business risk prediction result can be obtained.

Description

technical field [0001] The present application relates to the technical field of big data data analysis, and in particular, to a business risk prediction method, apparatus, computer equipment, storage medium and computer program product. Background technique [0002] With the gradual expansion of the demand for loans by enterprises and individuals, more and more risks are generated in the entire loan cycle. Therefore, the work of loan early warning is of great significance before, during and after the loan. [0003] When predicting loan risks, the prior art usually only pays attention to the impact of loan capital risks at the current moment on future moments, which is too one-sided and cannot accurately and efficiently predict the subsequent evolution process of loans, and cannot obtain accurate risk prediction results. [0004] It can be seen that the above-mentioned prior art has the problem of low accuracy of prediction results. SUMMARY OF THE INVENTION [0005] Based...

AI Technical Summary

Problems solved by technology

[0003] When the existing technology predicts the loan risk, it usually only pays attention to the impact of the loan capital risk at the current moment on the future moment, which is too one-sided and cannot accurately and efficiently predict the subsequent evolution of the loan, and cannot obtain accurate risk prediction results

[0004] It can be seen that the above prior art has the problem of low prediction result accuracy

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