Fraudulent transaction identification method, system and storage medium based on deep learning
A technology of deep learning and recognition methods, applied in the direction of neural learning methods, character and pattern recognition, payment systems, etc., can solve problems such as high difficulty and cost, demanding sample data, and inaccurate fraudulent transaction recognition methods, so as to reduce the difficulty and cost, improved accuracy, high tolerance effect
Active Publication Date: 2022-03-01
CHINA MERCHANTS BANK
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[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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[0048] It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.
[0049]The main solutions of the embodiments of the present invention are: obtaining training samples, which are transaction data used to establish a fraudulent transaction detection model; constructing a stacked RBM neural network structure, and training the stacked RBM based on the training samples A neural network structure, generating a dimension reducer; performing dimensionality reduction on the training samples through the dimensionality reducer, and clustering the binary state vectors obtained through dimensionality reduction to establish a fraudulent transaction detection model; obtaining the transaction to be detected data, and according to the fraudulent transaction detection model, analyze the transaction data to be detected to identify fraudulent transactions.
[0050] Such as figure 1 as shown, figure 1...
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The invention discloses a fraudulent transaction identification method, system and storage medium based on deep learning. The method includes: acquiring training samples, the training samples being transaction data for establishing a fraudulent transaction detection model; constructing a stacked RBM neural network structure, and train the stacked RBM neural network structure based on the training samples to generate a dimension reducer; perform dimensionality reduction on the training samples through the dimensionality reducer, and perform binary state vectors obtained through dimensionality reduction Clustering to establish a fraudulent transaction detection model; obtaining transaction data to be detected, and analyzing the transaction data to be detected according to the fraudulent transaction detection model to identify fraudulent transactions. The invention can improve the accuracy of identifying fraudulent transactions without predefining the similarity measurement method, thereby reducing difficulty and cost, and has high tolerance to sample data.
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, when unsupervised ...
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IPC IPC(8): G06Q20/40G06K9/62G06N3/08
CPCG06N3/088G06Q20/4016G06F18/23
Inventor 许泰清盛帅张文慧曾征曾卓然
Owner CHINA MERCHANTS BANK