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Original Article
Smart Fraud Detection in Online Payments Using Machine Learning
Ramesh K1
Santhosh Kumar S2
Sanjay S3
Thoushith4
S. Suman5
1 2 3 4 Department of Information Technology Er. Perumal Manimekalai College of Engineering Hosur, Tamil Nadu, India. 5 Assistant Professor, Guide, Department of Information Technology Er. Perumal Manimekalai College of Engineering Hosur,Tamil Nadu, India.
Published Online: March-April 2026
Pages: 430-437
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20260602061
References
1) J. Nilson, “The Nilson Report: Global Card Fraud Losses,” HSN Consultants, Inc., Issue 1278, Mar. 2024.
2) T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery
and Data Mining (KDD), San Francisco, CA, USA, pp. 785–794, Aug. 2016.
3) I. D. Mienye and Y. Sun, “A deep learning ensemble with data resampling for credit card fraud detection,” IEEE Access, vol. 11, pp.
30628–30638, 2023, doi: 10.1109/ACCESS.2023.3262020.
4) T. Martins, A. M. De Almeida, E. Cardoso, and L. Nunes, “Explain- able artificial intelligence (XAI): A systematic literature review on
taxonomies and applications in finance,” IEEE Access, vol. 12, pp. 618– 629, 2024, doi: 10.1109/ACCESS.2023.3347028.
5) N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic minority over-sampling technique,” J. Artif. Intell.
Res., vol. 16, pp. 321–357, 2002.
6) J. Xiao, “The application of machine learning in financial fraud detec- tion,” TechRxiv preprint, 2024, doi: 10.36227/techrxiv.1230727.
7) I. D. Mienye et al., “Financial fraud detection using explainable AI and stacking ensemble methods,” arXiv preprint, arXiv: 2505.10050,
2025.
8) J. C. Platt, “Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods,” Advances in Large
Margin Classifiers, MIT Press, pp. 61–74, 2000.
9) H. Wu, W. Liu, and E. Zheng, “Do time-aware fraud detection models suffer from temporal dependency leakage?” in Proc. ACM Int.
Conf. AI in Finance (ICAIF), pp. 64–72, 2023.
10) S. M. Lundberg and S. I. Lee, “A unified approach to interpreting model predictions,” in Advances in Neural Information Processing
Systems (NeurIPS), vol. 30, pp. 4765–4774, 2017.
11) E. A. Lopez-Rojas, A. Elmir, and S. Axelsson, “PaySim: A financial mobile money simulator for fraud detection,” in Proc. 28th European
Modeling and Simulation Symp. (EMSS), Larnaca, Cyprus, pp. 249–255, 2016.
12) A. Abid, A. Abdalla, A. Abid, D. Khan, A. Alfozan, and J. Zou, “Gradio: Hassle-free sharing and testing of ML models in the wild,”
arXiv preprint, arXiv:1906.02569, 2019.
13) C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger, “On calibration of modern neural networks,” in Proc. 34th Int. Conf. Machine Learning
(ICML), pp. 1321–1330, 2017.
14) A. Bhattacharyya, S. Jha, K. Tharakunnel, and J. C. Westland, “Data mining for credit card fraud: A comparative study,” Decision Support
Systems, vol. 50, no. 3, pp. 602–613, 2011.
15) Y. Duan et al., “CaT-GNN: Enhancing credit card fraud detec- tion via causal temporal graph neural networks,” arXiv preprint,
arXiv: 2402.14708, 2024.
16) Q. Yang, Y. Liu, T. Chen, and Y. Tong, “Federated machine learning: Concept and applications,” ACM Trans. Intell. Syst. Technol., vol.
10, no. 2, pp. 1–19, Mar. 2019.
2) T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery
and Data Mining (KDD), San Francisco, CA, USA, pp. 785–794, Aug. 2016.
3) I. D. Mienye and Y. Sun, “A deep learning ensemble with data resampling for credit card fraud detection,” IEEE Access, vol. 11, pp.
30628–30638, 2023, doi: 10.1109/ACCESS.2023.3262020.
4) T. Martins, A. M. De Almeida, E. Cardoso, and L. Nunes, “Explain- able artificial intelligence (XAI): A systematic literature review on
taxonomies and applications in finance,” IEEE Access, vol. 12, pp. 618– 629, 2024, doi: 10.1109/ACCESS.2023.3347028.
5) N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic minority over-sampling technique,” J. Artif. Intell.
Res., vol. 16, pp. 321–357, 2002.
6) J. Xiao, “The application of machine learning in financial fraud detec- tion,” TechRxiv preprint, 2024, doi: 10.36227/techrxiv.1230727.
7) I. D. Mienye et al., “Financial fraud detection using explainable AI and stacking ensemble methods,” arXiv preprint, arXiv: 2505.10050,
2025.
8) J. C. Platt, “Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods,” Advances in Large
Margin Classifiers, MIT Press, pp. 61–74, 2000.
9) H. Wu, W. Liu, and E. Zheng, “Do time-aware fraud detection models suffer from temporal dependency leakage?” in Proc. ACM Int.
Conf. AI in Finance (ICAIF), pp. 64–72, 2023.
10) S. M. Lundberg and S. I. Lee, “A unified approach to interpreting model predictions,” in Advances in Neural Information Processing
Systems (NeurIPS), vol. 30, pp. 4765–4774, 2017.
11) E. A. Lopez-Rojas, A. Elmir, and S. Axelsson, “PaySim: A financial mobile money simulator for fraud detection,” in Proc. 28th European
Modeling and Simulation Symp. (EMSS), Larnaca, Cyprus, pp. 249–255, 2016.
12) A. Abid, A. Abdalla, A. Abid, D. Khan, A. Alfozan, and J. Zou, “Gradio: Hassle-free sharing and testing of ML models in the wild,”
arXiv preprint, arXiv:1906.02569, 2019.
13) C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger, “On calibration of modern neural networks,” in Proc. 34th Int. Conf. Machine Learning
(ICML), pp. 1321–1330, 2017.
14) A. Bhattacharyya, S. Jha, K. Tharakunnel, and J. C. Westland, “Data mining for credit card fraud: A comparative study,” Decision Support
Systems, vol. 50, no. 3, pp. 602–613, 2011.
15) Y. Duan et al., “CaT-GNN: Enhancing credit card fraud detec- tion via causal temporal graph neural networks,” arXiv preprint,
arXiv: 2402.14708, 2024.
16) Q. Yang, Y. Liu, T. Chen, and Y. Tong, “Federated machine learning: Concept and applications,” ACM Trans. Intell. Syst. Technol., vol.
10, no. 2, pp. 1–19, Mar. 2019.
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