Risk rule extraction method and risk rule extraction device

Abstract

The embodiment of the invention discloses a risk rule extraction method and a risk rule extraction device, relates to the field of computer data mining, can output multivariable risk rules, and can meet the requirements of various risk control services. The method comprises the steps of obtaining N groups of sample data, wherein any one group of sample data in the N groups of sample data comprises attribute values corresponding to M variables, N is an integer greater than or equal to 2, and M is an integer greater than or equal to 2; processing the N groups of sample data by utilizing an automatic coding algorithm and an association rule algorithm, and determining X variables, wherein the risk coefficient of the sample data corresponding to the X variables is greater than a first threshold value and the correlation degree of the X variables is greater than a second threshold value, and the X variables belong to the M variables, and X is an integer smaller than or equal to M; and determining a risk rule according to the X variables, wherein the risk rule is used for indicating conditions met by risk events caused by the X variables.

Description

technical field [0001] The embodiments of the present application relate to the field of computer data mining, and in particular to a risk rule extraction method and a risk rule extraction device. Background technique [0002] At present, risk detection is required in many business scenarios, such as financial reimbursement anomaly detection, network intrusion detection, credit card fraud detection, health risk detection, product defect detection, and so on. Specifically, after the sample data is input, known rules are used to identify the abnormalities of the input sample data, and abnormal sample data that do not conform to the known rules are output. In addition, abnormal sample data can also be learned to generate univariate risk rules, that is, rules that cause risk by one variable. [0003] In the existing technology, relying on the known rules of a certain business to identify risks can only be oriented to a single business scenario and cannot be flexibly applied to ...

AI Technical Summary

Problems solved by technology

[0003] In the existing technology, relying on the known rules of a certain business to identify risks can only be oriented to a single business scenario and cannot be flexibly applied to other business scenarios

In addition, the existing technology can only extract univariate risk rules, and the filtering ability of univariate rules is low, which cannot adapt to the diversity of scenarios and cannot meet the increasingly complex risk control business needs

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