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Risk identification method and system based on transfer deep learning

A technology of risk identification and deep learning, applied in the field of transaction risk identification method and system based on migration deep learning, can solve the problem of difficult identification of unknown fraud types, and achieve the effect of accurate identification

Active Publication Date: 2018-02-09
CHINA UNIONPAY
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  • Abstract
  • Description
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AI Technical Summary

Problems solved by technology

[0008] Difficult to identify unknown fraud types lacking fraud samples

Method used

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  • Risk identification method and system based on transfer deep learning
  • Risk identification method and system based on transfer deep learning
  • Risk identification method and system based on transfer deep learning

Examples

Experimental program
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Embodiment Construction

[0043] Introduced below are some of the various embodiments of the invention, intended to provide a basic understanding of the invention. It is not intended to identify key or critical elements of the invention or to delineate the scope of protection.

[0044] First, several concepts to be mentioned in the present invention are described.

[0045] (1) Restricted Boltzmann Machine (RBM)

[0046] RBM is a stochastically generated neural network that learns a probability distribution over an input dataset. RBM is a variant of Boltzmann machine, but the limited model must be a bipartite graph. The model contains visible units corresponding to input parameters (hereinafter also referred to as visible layers) and hidden units corresponding to training results (hereinafter also referred to as hidden layers), and each edge must connect a visible unit and a hidden unit.

[0047] (2) BP algorithm (that is, error backpropagation algorithm)

[0048] The BP algorithm is a learning algori...

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Abstract

The invention relates to a risk identification method and system based on transfer deep learning. The risk identification method comprises the steps of generating vectors for all possible features through prescribed preprocessing, enabling the vector set to act as visible layer input of a first RBM ((Restricted Boltzmann Machine) so as to build an RBM layer; performing transfer learning by using known fraud samples, and carrying out transfer weighted BP tuning on the RBM layer built in RBM building step; and judging whether the RBM meets prescribed conditions or not after BP tuning, if the RBMmeets the prescribed conditions, not requiring to increase an RBM layer and continuing the following step, and if the RBM does not meet the prescribed conditions, repeating the steps of RBM buildingand transfer weighted BP tuning. A judgment model can be built more accurately and emerging fraud means can be better dealt with according to the invention.

Description

technical field [0001] The present invention relates to computer technology, and more specifically to a transaction risk identification method and system based on migration deep learning. Background technique [0002] In the process of using machine learning to identify fraud risks, currently supervised classification algorithms are generally used to train detection models. Traditional classification learning algorithms require feature selection and computation in advance. A large part of these features used to train the model (especially those features that have been obtained through statistics) are deduced based on the laws summarized in the historical fraud data sets, which requires a lot of experience accumulation, and inevitably omissions. [0003] At the same time, when using historical transaction data for fraud risk identification model training, there is a serious data imbalance, that is, the number of samples with fraud labels is far smaller than the number of non...

Claims

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Application Information

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IPC IPC(8): G06Q20/40G06N3/08
CPCG06N3/084G06Q20/4016
Inventor 李旭瑞邱雪涛赵金涛胡奕
Owner CHINA UNIONPAY
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