Deep learning-based fraud transaction recognition method, fraud transaction recognition system and storage medium

Abstract

The invention discloses a deep learning-based fraud transaction recognition method, a fraud transaction recognition system and a storage medium. The method comprises the following steps: acquiring training samples, wherein the training sample is composed of transaction data used for establishing a fraud transaction detection model; constructing a stacked restricted Boltzmann machine RBM neural network structure, training the RBM neural network structure, and carrying out dimensionality reduction and clustering treatment on the training sample through the trained RBM neural network structure soas to divide the training sample into a plurality of groups; calculating the mass center of each of all the groups, and respectively calculating the hamming distance between each group and the mass center; determining the fraud probability of each group according to the calculated hamming distance so as to establish a fraud transaction detection model; acquiring to-be-detected transaction data, and analyzing the to-be-detected transaction data according to the fraud transaction detection model so as to obtain the fraud probability of the to-be-detected transaction data. In this way, the fraudtransaction is recognized. By means of the method and the device, the accuracy and the rationality of fraud transaction recognition can be improved.

Description

technical field

[0001] The present invention relates to the field of financial risk control, in particular to a method, system and storage medium for identifying fraudulent transactions based on deep learning. Background technique

[0002] The financial sector has higher requirements for transaction risk control. In the identification of fraudulent transactions using deep learning, currently supervised learning algorithms are generally used to train detection models, and the features used to train detection models are constructed based on labeled historical transaction data, so the detection models trained by supervised learning algorithms , can effectively identify historical fraud types, but generally can’t do anything about unknown fraud types that lack fraud samples (such as fraudulent transactions that have never appeared or have variants). This posteriority leads to lag in transaction risk identification and low accuracy.

[0003] On the other hand, the existing metho...

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