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Method and device for establishing mixed fraudulent trading detection classifier

A classifier and transaction technology, applied in the computer field, can solve the problems of large randomness, insignificant effect improvement, and lack of physical meaning.

Pending Publication Date: 2017-05-10
CHINA UNIONPAY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This approach lacks the support of physical meaning and has greater randomness, so the effect improvement is not significant
[0004] In summary, there is still a lack of a model training method that can improve the detection accuracy of mixed fraudulent transactions

Method used

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  • Method and device for establishing mixed fraudulent trading detection classifier
  • Method and device for establishing mixed fraudulent trading detection classifier
  • Method and device for establishing mixed fraudulent trading detection classifier

Examples

Experimental program
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Embodiment Construction

[0085] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments . Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0086] figure 1 It is a schematic flowchart of a method for establishing a hybrid fraud detection classifier provided by an embodiment of the present invention, as shown in figure 1 shown, including the following steps:

[0087] S101: Obtain transaction sample data within a set time period, where the transaction sample data includes normal transaction samples and fraudulent transaction samples;

[0088] S102: Determine a typical fraudule...

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Abstract

The embodiment of the invention provides a method and a device for establishing a mixed fraudulent trading detection classifier, and is used for providing a model training method capable of improving mixed fraudulent trading detection accuracy. The method comprises the following steps of: obtaining normal trading samples and fraudulent trading samples; determining a typical fraudulent trading sample set from the fraudulent trading samples; clustering the normal trading samples to obtain N typical normal trading sample sets, wherein n is a positive integer; independently training the typical fraudulent trading sample set and each typical normal trading sample set to obtain N classifiers; and fusing the N classifiers to obtain the fraudulent trading classifier. The typical fraudulent trading sample set is extracted to improve accuracy for carrying out mixed fraudulent detection on the classifier, and normal samples subjected to rough clustering can be extracted through a graph intersection and union method to alleviate adverse effects brought by data imbalance. After sub-classifiers are fused into one strong classifier, the strong classifier can be used for detecting mixed fraudulent trading.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a method and device for establishing a mixed fraud transaction detection classifier. Background technique [0002] With the development of Internet finance, people's payment habits have undergone major changes, followed by increasingly rampant transaction fraud, which has brought unprecedented challenges to the existing fraud detection system. Many rule-based or machine learning-trained models have been proposed for fraud detection. [0003] However, there are many kinds of fraud in real transactions, such as counterfeit card fraud, stolen card fraud, and non-delivered card fraud, and today's fraudulent transactions are mostly mixed fraudulent transactions, that is, fraudulent transactions that are composed of multiple types of fraudulent transactions , if simply combine all fraudulent samples and normal samples to train the model uniformly, the results obtained are often not ...

Claims

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Application Information

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IPC IPC(8): G06Q20/38G06K9/62
CPCG06Q20/382G06F18/23G06F18/24G06F18/214
Inventor 李旭瑞邱雪涛赵金涛刘红宝胡一非
Owner CHINA UNIONPAY
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