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

Abstract

The invention discloses a deep learning-based fraud transaction identification method, system and a storage medium. The method comprises the following steps: acquiring a training sample, wherein the training sample is transaction data for establishing a fraud transaction detection model; constructing a stacked RBM neural network structure; training the stacked RBM neural network structure based onthe training sample, and generating a dimension reducer; performing dimension reduction on the training sample via the dimension reducer, and clustering the binary state vector obtained by dimensionreduction so as to establish a fraud transaction detection model; obtaining transaction data to be detected, and analyzing the transaction data to be detected according to the fraud transaction detection model so as to identify fraudulent transactions. The deep learning-based fraud transaction identification method, system and the storage medium in the invention can improve the accuracy of fraudulent transaction identification, and does not need to define a similarity measurement method in advance, thereby reducing difficulty and cost, and the high tolerance to sample data is achieved.

Description

Technical field [0001] The 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 recognition of fraudulent transactions using deep learning, currently supervised learning algorithms are generally used to train detection models, and the features used to train the detection models are constructed based on labeled historical transaction data, so detection models trained by supervised learning algorithms are used , It can effectively identify historical fraud types, but it is generally incapable of unknown fraud types that lack fraud samples (such as fraudulent transactions that have never appeared or variants). This posterior nature leads to lagging and low accuracy in transaction risk identification. [0003] On the other hand, when the existing unsuperv...

AI Technical Summary

Problems solved by technology

[0004] The main purpose of the present invention is to provide a method for identifying fraudulent transactions based on deep learning, which aims to solve the technical problems that the existing methods for identifying fraudulent transactions are not accurate enough, have great difficulty and cost, and are demanding on sample data

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