Credit Card Fraud Detection for Contemporary Financial Management Using XGBoost-Driven Machine Learning and Data Augmentation Techniques

Journal article

T. R. Noviandy, Ghalieb Mutig Idroes, A. Maulana, Irsan Hardi, Edi Saputra Ringga, R. Idroes
Indatu Journal of Management and Accounting, 2023

Semantic Scholar DOI

Cite

APA Click to copy
Noviandy, T. R., Idroes, G. M., Maulana, A., Hardi, I., Ringga, E. S., & Idroes, R. (2023). Credit Card Fraud Detection for Contemporary Financial Management Using XGBoost-Driven Machine Learning and Data Augmentation Techniques. Indatu Journal of Management and Accounting.

Chicago/Turabian Click to copy
Noviandy, T. R., Ghalieb Mutig Idroes, A. Maulana, Irsan Hardi, Edi Saputra Ringga, and R. Idroes. “Credit Card Fraud Detection for Contemporary Financial Management Using XGBoost-Driven Machine Learning and Data Augmentation Techniques.” Indatu Journal of Management and Accounting (2023).

MLA Click to copy
Noviandy, T. R., et al. “Credit Card Fraud Detection for Contemporary Financial Management Using XGBoost-Driven Machine Learning and Data Augmentation Techniques.” Indatu Journal of Management and Accounting, 2023.

BibTeX Click to copy

@article{t2023a,
  title = {Credit Card Fraud Detection for Contemporary Financial Management Using XGBoost-Driven Machine Learning and Data Augmentation Techniques},
  year = {2023},
  journal = {Indatu Journal of Management and Accounting},
  author = {Noviandy, T. R. and Idroes, Ghalieb Mutig and Maulana, A. and Hardi, Irsan and Ringga, Edi Saputra and Idroes, R.}
}

Abstract

The rise of digital transactions and electronic payment systems in modern financial management has brought convenience but also the challenge of credit card fraud. Traditional fraud detection methods are struggling to cope with the complexities of contemporary fraud strategies. This study explores the potential of machine learning, specifically the XGBoost (eXtreme Gradient Boosting) algorithm, combined with data augmentation techniques, to enhance credit card fraud detection. The research demonstrates the effectiveness of these techniques in addressing imbalanced datasets and improving fraud detection accuracy. The study showcases a balanced approach to precision and recall in fraud detection by leveraging historical transaction data and employing techniques like Synthetic Minority Over-sampling Technique-Edited Nearest Neighbors (SMOTE-ENN). The implications of these findings for contemporary financial management are profound, offering the potential to bolster financial integrity, allocate resources effectively, and strengthen customer trust in the face of evolving fraud tactics.