Case risk detection method and system

Abstract

The invention discloses a case risk detection method and system. The method comprises the steps of starting a case risk detection database; Selecting a service unit matched with the to-be-detected case, and finding out a risk model matched with the case from the service unit; Obtaining all risk keyword trees of the risk model, iterating each risk keyword sub-node of all risk keyword trees, judgingwhether the risk keyword sub-nodes contain risks or not according to a detection mode of the risk keyword sub-nodes, and outputting a risk path where the risk keyword sub-nodes with the risks are located and the risk level of each risk keyword sub-node; And counting the number of risk levels in all risk paths containing risks, and obtaining a risk rating result of the case according to a risk rating calculation rule configured by the risk model. Accurate calculation of risk detection in legal cases is achieved, large errors caused by subjective judgment of risk values are avoided, and case risk detection efficiency is improved.

Description

technical field [0001] The invention relates to the field of risk detection, in particular to a method and system for detecting risks of legal cases. Background technique [0002] As my country's legal system continues to improve, the risk detection of legal cases in various businesses is becoming more and more important. At present, the risk information of the legal case system is managed by maintaining risk tags and selecting relevant tags for the case through human identification. After saving, the risky result of the case is promoted, and the risk information of the case is identified. The disadvantages of this method are: first, if human judgment is wrong, there will be cases where maintenance results are wrong, and the result depends entirely on human conscious judgment, which is risky; second, in the process of human judgment, it may be necessary to repeatedly learn from Historical data is used as a reference, the workload is very huge, the possibility of judgment er...

AI Technical Summary

Problems solved by technology

The disadvantages of this method are: first, if human judgment is wrong, there will be cases where maintenance results are wrong, and the result depends entirely on human conscious judgment, which is risky; second, in the process of human judgment, it may be necessary to repeatedly learn from Historical data is used as a reference, the workload is very huge, the possibility of judgment errors in the whole process is high, and the efficiency is very low; third, the risk results show hash and single, and the reference significance is not obvious

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