Intelligent risk management rule export method and system based on decision-making tree

A decision tree and rule technology, applied in the direction of instrumentation, finance, data processing applications, etc., can solve problems such as lack of interpretability, long formulation cycle, and difficulty for business personnel to trace the basis of model judgment and implementation logic, etc., to achieve good interpretability Effect
CN107545360AInactive Publication Date: 2018-01-05ZHEJIANG BANGSUN TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
ZHEJIANG BANGSUN TECH CO LTD
Publication Date
2018-01-05
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention discloses a method and system for deriving risk control intelligent rules based on a decision tree. According to the importance of the features, the invention sorts its huge number of features, screens out important features, and builds decision trees of different depths based on these features. Then use the set threshold to filter the decision tree, and finally derive the rules according to the filtered decision tree. The method of the invention can ensure that under the normal operation of the business system, rules can be layered and derived according to different feature numbers, and fraudulent behavior can be detected to the greatest extent. Compared with the risk control system with artificial rules, the system of the present invention is more stable and intelligent, and the efficiency of intelligent rules is higher, so that the loss of enterprises can be minimized. Especially in systems with complex business and huge data volume, this advantage becomes more and more obvious.
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Description

technical field

[0001] The present invention relates to the derivation technology of risk control rules, in particular to a method and system for deriving risk control intelligent rules based on a decision tree. Background technique

[0002] Risk control rules have extensive application value in most Internet and financial companies in today's society. In most cases, most of the risk control rules are formulated by relevant business personnel and security experts based on past experience, business and other conditions. This kind of rule-making cycle is relatively long and cannot be fully applied to different business systems. That is, when another business scenario is encountered, experts need to conduct business analysis and decision-making again. This will bring greater economic losses and overhead costs to the enterprise. Compared with the traditional credit scorecard model, the traditional machine learning algorithm usually lacks explanation as a black box model, and i...

Claims

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