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

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

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 (G...

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Bibliographic Details
Main Authors: Patience Chew Yee Cheah, Yue Yang, Boon Giin Lee
Format: Article
Language:English
Published: MDPI AG 2023-09-01
Series:International Journal of Financial Studies
Subjects:
class imbalance
data generation
deep learning
financial fraud detection
Online Access:https://www.mdpi.com/2227-7072/11/3/110

Internet

https://www.mdpi.com/2227-7072/11/3/110

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