An abnormal transaction identification method based on heterogeneous financial features, equipment and storage medium

A technology of abnormal transaction and identification method, which is applied in the field of abnormal transaction identification to achieve the effect of detecting and identifying abnormal financial transaction relationships, improving work efficiency, and enriching abstraction and expression.

Active Publication Date: 2022-03-11
HARBIN INST OF TECH AT WEIHAI
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0003] At present, there is no effective machine learning solution based on heterogeneous financial characteristics, which can effectively detect and identify abnormal financial transaction relationships

Method used

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  • An abnormal transaction identification method based on heterogeneous financial features, equipment and storage medium
  • An abnormal transaction identification method based on heterogeneous financial features, equipment and storage medium
  • An abnormal transaction identification method based on heterogeneous financial features, equipment and storage medium

Examples

Experimental program
Comparison scheme
Effect test

specific Embodiment

[0125] A user enters the original financial transaction flow annotation data set The data set to be tested with the original financial transaction flow

[0126] Step one, assume There are 100 MLM accounts and 100 normal accounts each, and each account has more than 100 transaction data, that is, MLM and normal accounts have about 10,000 transaction data respectively. There are 50 accounts to be tested, and each account also has about 100 transaction flow data, a total of about 5,000 transaction flow data.

[0127] Step 2, separately for the input data and Carry out data preprocessing, do data cleaning and data item format normalization processing, and then perform key item data extraction to obtain the processing result key item data sets D and D respectively test . Then, the information data set and the account information data set are respectively constructed to obtain the transaction pair information data set D tp , and account information dataset D c , bu...

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Abstract

The present invention provides a method for identifying abnormal transactions based on heterogeneous financial features, equipment and storage media, which can extract user-defined transaction entropy features and Transaction activity, amount statistical features, and construction of heterogeneous financial feature vector representation, and then based on the heterogeneous feature vector, use the voting classifier to classify and identify whether the transaction account to be detected is an abnormal transaction relationship of pyramid schemes. The suspicious financial transaction relationship identification method proposed by the present invention utilizes the financial transaction flow data to realize richer abstraction and expression of complex behavioral characteristics of transaction subjects, and achieve better detection and identification effects on abnormal financial transaction relationships. The abnormal financial transaction relationship detection results provided by this method can assist relevant staff in the investigation and judgment of abnormal financial activities such as pyramid schemes, and can improve work efficiency and accuracy of research and judgment to a certain extent.

Description

technical field [0001] The present invention relates to the field of financial transactions, in particular to an abnormal transaction identification method based on heterogeneous financial features, equipment and a storage medium. Background technique [0002] The financial system is one of the important pillars of modern economic development. With the development of the network and information technology of the financial system, the capital flow is accelerating, and the daily transaction flow of financial transaction institutions such as banks can reach millions or even tens of millions. . In the massive transaction data, there are more complex customer transaction behavior patterns and rules and other in-depth information, which can be used to identify and detect abnormal transaction behavior hidden in the data. [0003] At present, there is no effective machine learning scheme based on heterogeneous financial features, which can effectively detect and identify abnormal f...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q20/38G06Q40/04
CPCG06Q20/382G06Q40/04
Inventor 李晓颖吕芳王佰玲王巍黄俊恒辛国栋
Owner HARBIN INST OF TECH AT WEIHAI
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