In-depth detection method for transaction fraud based on feature differentiation

Abstract

The invention relates to a deep detection method for transaction fraudulent behavior based on feature differentiation, and is characterized in that a transaction time-based differentiation feature generation method and a fraudulent transaction detection method with outlier sample detection are proposed. The network transaction fraud detection method proposed by the present invention can effectively detect fraudulent behaviors in network transactions. From the perspective of practicability, the method provided by the present invention establishes a method for generating a differentiated feature and detecting a fraudulent transaction with outlier sample detection. Developed a network transaction fraud detection system and provided technical support for solving fraudulent transaction detection.

Description

technical field [0001] The invention relates to a network transaction detection method. Background technique [0002] A large body of research has emerged in the field of machine learning algorithms for fraudulent transaction detection, including studies using classification methods such as decision trees, neural networks, Bayesian networks, and random forests. In 2013, Yusuf Sahin et al. proposed a new cost-sensitive decision tree detection technology, and the model showed superior performance to traditional data mining methods such as SVM. In 2014, Kolalikhormuji et al. proposed the use of cascaded artificial neural networks to improve the recognition rate and reduce the rejection rate, and set the gating network to aggregate three parallel neural networks, which performed well on the credit card data of a large bank in Brazil. In 2015, Chengwei Liu et al. compared the performance of the four detection methods of SVM, logictic regression, KNN and random forest in financia...

AI Technical Summary

Problems solved by technology

Compared with credit card fraud detection, online transactions are real-time, have high transaction frequency, and are diversified in terms of transaction types. Therefore, credit card fraud detection methods are not completely suitable for network transaction fraud detection.

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