Fraudulent transaction identification method, system and device based on dynamic weighted information entropy

Abstract

The invention provides a fraudulent transaction identification method, system and device based on dynamic weighted information entropy. The method comprises the following steps: screening one-class-SVM models through the dynamic weighted information entropy, and selecting a one-class-SVM model Mocsvm with the maximum dynamic weighted information entropy of an overlapped data subset; dividing original data into an overlapped data subset and a non-overlapped data subset by using a one-class-SVM model Mocsvm; training a non-linear classifier model Mclf is by using an overlapping data subset obtained through division of a one-class-SVM model Mocsvm, and distinguishing fraudulent transactions and normal transactions in the overlapping data subset by using the non-linear classifier model Mclf; and generating a fraudulent transaction identification model composed of the one-class-SVM model Mocsvm and the nonlinear classifier model Mclf. According to the method, a division and treatment strategy is adopted, and a large amount of normal transaction data easy to recognize is discharged for a nonlinear machine learning model, so the model can only pay attention to learning of data difficult to divide, the capacity of the nonlinear model is fully played, and the performance of a fraudulent transaction identification model is improved.

Description

technical field [0001] The invention relates to the technical field of electronic fraudulent transaction identification, in particular to a fraudulent transaction identification method, system and device based on dynamic weighted information entropy. Background technique [0002] In recent years, financial technology, which integrates finance and technology, has become one of the hot research fields. Artificial intelligence has promoted financial technology to provide higher-quality services. At the same time, financial technology has provided a wide range of platforms and application scenarios for artificial intelligence research and innovation. Fraud detection in electronic transactions is one of the most important studies in fintech and has attracted extensive attention. Identifying fraudulent transactions is very challenging, and one of the most important reasons is the data imbalance problem, especially the data imbalance problem with data overlapping (overlapping). C...

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Problems solved by technology

[0006] In view of the shortcomings of the prior art described above, the purpose of the present invention is to provide a method, system and device for identifying fraudulent transactions based on dynamic weighted information entropy, which is used to solve how to effectively and quickly identify overlapping and non-overlapping in the prior art Data subsets, how to speed up the training process of the subsequent nonlinear machine learning model, reduce the resource consumption of model training, and how to better identify electronic fraud transactions

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