Enhancing Financial Fraud Detection through Addressing Class Imbalance Using Hybrid SMOTE-GAN Techniques

Patience Chew Yee Cheah, Yue Yang and Boon Giin Lee ()
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Patience Chew Yee Cheah: School of Computer Science, University of Nottingham Ningbo China, Ningbo 315100, China
Yue Yang: School of Computer Science, University of Nottingham Ningbo China, Ningbo 315100, China
Boon Giin Lee: School of Computer Science, University of Nottingham Ningbo China, Ningbo 315100, China

IJFS, 2023, vol. 11, issue 3, 1-17

Abstract: The class imbalance problem in finance fraud datasets often leads to biased prediction towards the nonfraud class, resulting in poor performance in the fraud class. This study explores the effects of utilizing the Synthetic Minority Oversampling TEchnique (SMOTE), a Generative Adversarial Network (GAN), and their combinations to address the class imbalance issue. Their effectiveness was evaluated using a Feed-forward Neural Network (FNN), Convolutional Neural Network (CNN), and their hybrid (FNN+CNN). This study found that regardless of the data generation techniques applied, the classifier’s hyperparameters can affect classification performance. The comparisons of various data generation techniques demonstrated the effectiveness of the hybrid SMOTE and GAN, including SMOTified-GAN, SMOTE+GAN, and GANified-SMOTE, compared with SMOTE and GAN. The SMOTified-GAN and the proposed GANified-SMOTE were able to perform equally well across different amounts of generated fraud samples.

Keywords: class imbalance; data generation; deep learning; financial fraud detection (search for similar items in EconPapers)
JEL-codes: F2 F3 F41 F42 G1 G2 G3 (search for similar items in EconPapers)
Date: 2023
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