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Research on Credit Card Fraud Detection Based on GAN

Published: 19 April 2023 Publication History

Abstract

With the advancement of technology, credit card transactions have become more and more popular, and the number of credit card frauds has also increased at the same time. In order to reduce property loss of cardholders and banks, many traditional machine learning algorithms based on binary classification are applied to detect fraudulent transactions. However, the number of fraudulent transactions and normal transactions in the dataset used to train the classifier is seriously imbalanced, which causes the classifier with the goal of accuracy to tend to classify all transactions as normal transactions. It is meaningless to detect fraudulent transactions with this trained classifier. Oversampling the minority fraudulent transaction classes to rebalance the training set is an effective way to address the problem of class imbalance. The method proposed in this paper uses an improved conditional generative adversarial network to rebalance dataset, then combines with random forests classifier for fraud detection, and experiments prove that the proposed method is effective for credit card fraud detection.

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    RICAI '22: Proceedings of the 2022 4th International Conference on Robotics, Intelligent Control and Artificial Intelligence
    December 2022
    1396 pages
    ISBN:9781450398343
    DOI:10.1145/3584376
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 19 April 2023

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