Billing data risk control method, device, computer equipment and storage medium

Abstract

The present application provides an invoicing data risk control method, device, computer equipment and storage medium. The method includes: an invoicing risk analysis engine performs data processing and calculation on the invoicing data stream flowing in a preset time window to obtain the risk control dimensions of each invoice. Based on the current risk control calculation results and the accumulated risk control calculation results under the same invoice risk control dimension, the current cumulative risk control calculation results under each invoice risk control dimension are obtained; The current risk control rule group is iteratively selected in descending order of the risk level corresponding to the rule group, and the current cumulative risk control calculation results under each invoice risk control dimension are matched with each risk control rule item in the current risk control rule group. , and stop the iteration after hitting the risk control rule group; determine the target risk level corresponding to the hit target risk control rule group; perform billing risk early warning according to the early warning method corresponding to the target risk level. This scheme can improve the efficiency of invoicing and early warning.

Description

technical field [0001] The present application relates to the field of computer technology, and in particular, to a method, device, computer equipment and storage medium for risk control of billing data. Background technique [0002] With the rapid development of science and technology, various advanced technologies continue to emerge. In the field of finance and taxation, the security of data is very important, so the risk control technology in the field of finance and taxation has been paid more and more attention. How to supervise every online transaction is a major pain point in the field of finance and taxation. [0003] For the field of finance and taxation, the traditional early warning processing is still offline, and the massive billing data is analyzed offline, which will lead to the output of the analysis results being late and not timely enough, resulting in the risk warning is not timely enough. Therefore, , there is the disadvantage of poor timeliness of bill...

AI Technical Summary

Problems solved by technology

[0003] For the field of finance and taxation, the traditional early warning processing is still offline, and the massive invoicing data is analyzed offline, which will lead to the late output of the analysis results, which is not timely enough, resulting in the risk early warning is not timely enough, so , there is the disadvantage of poor timeliness of invoicing risk warning

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