Method for detecting fraud behavior of merchant category code cloning

A technology of merchant category code and detection method, which is applied in the field of information classification processing

Inactive Publication Date: 2015-12-23
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Abstract
  • Description
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Problems solved by technology

[0004] The purpose of the present invention is to overcome the above-mentioned deficiencies in manual detection, to provide a method for detectin...

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  • Method for detecting fraud behavior of merchant category code cloning
  • Method for detecting fraud behavior of merchant category code cloning
  • Method for detecting fraud behavior of merchant category code cloning

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Embodiment Construction

[0037] Specific embodiments of the present invention will be described below in conjunction with the accompanying drawings, so that those skilled in the art can better understand the present invention. It should be noted that in the following description, when detailed descriptions of known functions and designs may dilute the main content of the present invention, these descriptions will be omitted here.

[0038] The invention provides a method for detecting fraudulent behaviors using merchant category codes by using data mining methods such as clustering, classification, and abnormality detection. The detection of fraudulent behavior using merchant category codes is a quantitative judgment of the possibility of fraud. First, a separator model is constructed based on the historical transaction data of normal merchants, and then the transaction behavior of new merchants is analyzed and processed using the model to identify the merchant. Whether it is an application merchant. ...

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Abstract

The invention discloses a method for detecting the fraud behavior of merchant category code cloning, comprising the steps as follows: first, acquiring and preprocessing transaction data of normal merchants, and carrying out hierarchical clustering to acquire the standard behavior pattern; then, randomly extracting transaction flow data of normal merchants and cloned merchants according to the rule that the ratio of the number of normal merchants to the number of cloned merchants is 1:1, acquiring an 86-dimension transaction data vector of each merchant, acquiring a 9-dimension feature through Euclidean distance calculation, carrying out superposition to obtain 95-dimension feature vectors, and carrying out training constantly by a batch gradient descent method to obtain the parameters of a logistic regression model and to obtain a classifier; and finally, constructing a 95-dimension feature vector according to the one-year transaction flow data of a new user, sending the 95-dimension feature vector to the classifier for classification, and detecting whether the new user is a cloned merchant. By using a computer system, a merchant behavior can be identified from multiple dimensions, and therefore, merchant cloning can be detected efficiently and accurately.

Description

technical field [0001] The invention belongs to the technical field of information classification and processing, and more specifically relates to a method for detecting fraudulent behavior by applying merchant category codes, which is used to identify merchants who use merchant category codes to carry out fraud. Background technique [0002] In recent years, with the rapid development of the economy, POS machines (Point of Sale, point of sale information management system) have also been popular among merchants. Merchants who install POS machines involve all walks of life, and different industries charge different service fees for the use of POS machines. Some third-party payment institutions apply merchant category codes to enjoy low service fee rates are also increasing day by day. The fraudulent behavior of the merchant category code caused huge losses to the card issuer, seriously disrupted the financial order of the POS acquiring industry, caused insecurity in the POS ...

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Application Information

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IPC IPC(8): G06Q30/00
Inventor 刘震林霞霞傅彦李忠洵
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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