Open AccessA Data Simulation Method of Bank Fraud Transaction Based on Flow-Based Generative Model
Author(s) -
Xiaoguo Wang,
Yin Zhang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1631/1/012103
Subject(s) - generative model , database transaction , transaction data , computer science , economic shortage , generative grammar , synthetic data , flow (mathematics) , mixing (physics) , data mining , order (exchange) , data flow diagram , artificial intelligence , finance , database , business , mathematics , linguistics , philosophy , physics , geometry , quantum mechanics , government (linguistics)
In order to solve the problem of category imbalance caused by the shortage of bank fraud transaction data, this paper proposes a bank fraud transaction data simulation method based on flow-based generative model. On the basis of the flow-based generative model and the real fraudulent transaction data of the bank, this method designs a generative model suitable for the bank data, and learns the distribution of the real data through the generators G and G -1 . The experimental results show that mixing the generated simulated data and real business data in a certain proportion to train the fraud detection model can improve the detection effect of the model to a certain extent.