Non-Ideal Victims or Offenders? The Curious Case of Pyramid Scheme Participants

Hock, Branislav; Button, Mark

doi:10.1080/15564886.2023.2186996

Cited by 6 publications

(1 citation statement)

References 58 publications

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“…The detection of credit card fraud within the financial sector could potentially be integrated with the field of economic criminology [12]. The efficiency and effectiveness of fraud detection systems could be enhanced by recognizing the limitations inherent in policing fraud through the application of modern technologies [13]. Leveraging the continual progress in machine learning (ML), a broad array of diverse ML systems is deployed for credit card fraud detection across various datasets.…”

Section: Introductionmentioning

confidence: 99%

Novel Machine Learning Based Credit Card Fraud Detection Systems

Feng,

Kim

2024

Mathematics

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This research deals with the critical issue of credit card fraud, a problem that has escalated in the last decade due to the significant increase in credit card usage, largely driven by advances in international trade, e-commerce, and FinTech. With global losses projected to exceed USD 400 billion in the next decade, the urgent need for effective fraud detection systems is apparent. Our study leverages the power of machine learning (ML) and presents a novel approach to credit card fraud detection. We used the European cardholders dataset for model training, addressing the data imbalance issue that often hinders the effectiveness of the learning process. As a key innovative element, we introduce compact data learning (CDL), a powerful tool for reducing the size and complexity of the training dataset without sacrificing the accuracy of the ML system. Comparative experiments have shown that our CDL-adapted feature reduction outperforms various ML algorithms and feature reduction methods. The findings of this research not only contribute to the theoretical foundations of fraud detection but also provide practical implications for the financial sector, which can benefit immensely from the enhanced fraud detection system.

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Section: Introductionmentioning

confidence: 99%