Real Time Fraud Detection at Scale in High Volume Enterprise Payment Ecosystems

Yuxuan Qin; Jiesi Yang; Zimeng Wang

doi:10.54097/51td8c59

Authors

Yuxuan Qin
Jiesi Yang
Zimeng Wang

DOI:

https://doi.org/10.54097/51td8c59

Keywords:

Real-time fraud detection, Enterprise payment systems, Machine learning, Deep learning, Graph neural networks, Streaming analytics, Anomaly detection, Federated learning

Abstract

The rapid proliferation of digital payment channels has created unprecedented opportunities for fraudulent activity, compelling enterprise organizations to deploy increasingly sophisticated detection mechanisms capable of operating at high throughput with minimal latency. This paper presents a comprehensive review of real-time fraud detection (RTFD) methodologies applied within high-volume enterprise payment ecosystems, encompassing machine learning (ML), deep learning (DL), graph neural networks (GNN), and streaming data architectures. We examine how ensemble methods, anomaly detection frameworks, and feature engineering pipelines converge to form robust, production-grade fraud detection systems (FDS). The paper further discusses the tension between detection accuracy and operational latency, the challenge of class imbalance in transactional datasets, and the evolving regulatory landscape that shapes deployment constraints. By synthesizing findings from recent literature, this review identifies key trends including federated learning (FL) for privacy-preserving fraud detection, transformer-based sequence models for behavioral analysis, and adaptive threshold mechanisms for dynamic fraud pattern recognition. Our analysis reveals that no single algorithmic approach suffices in isolation; rather, layered architectures combining rule-based systems with data-driven models consistently achieve superior performance across precision, recall, and throughput metrics in enterprise-scale deployments.

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References

[1] Bonett, S., Lin, W., Sexton Topper, P., Wolfe, J., Golinkoff, J., Deshpande, A., ... & Bauermeister, J. (2024). Assessing and improving data integrity in web-based surveys: comparison of fraud detection systems in a COVID-19 study. JMIR formative research, 8, e47091.

[2] Rtayli, N., & Enneya, N. (2020). Enhanced credit card fraud detection based on SVM-recursive feature elimination and hyper-parameters optimization. Journal of Information Security and Applications, 55, 102596.

[3] Zhang, X., Han, Y., Xu, W., & Wang, Q. (2021). HOBA: A novel feature engineering methodology for credit card fraud detection with a deep learning architecture. Information sciences, 557, 302-316.

[4] Saia, R., & Carta, S. (2019). Evaluating the benefits of using proactive transformed-domain-based techniques in fraud detection tasks. Future Generation Computer Systems, 93, 18-32.

[5] Sriram, H. K. (2024). Leveraging AI and machine learning for enhancing secure payment processing: A study on generative AI applications in real-time fraud detection and prevention. Available at SSRN 5203586.

[6] Lucas, Y., Portier, P. E., Laporte, L., He-Guelton, L., Caelen, O., Granitzer, M., & Calabretto, S. (2020). Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs. Future Generation Computer Systems, 102, 393-402.

[7] Carcillo, F., Le Borgne, Y. A., Caelen, O., Kessaci, Y., Oblé, F., & Bontempi, G. (2021). Combining unsupervised and supervised learning in credit card fraud detection. Information sciences, 557, 317-331.

[8] Kaur, M., & Mohta, A. (2019, November). A review of deep learning with recurrent neural network. In 2019 international conference on smart systems and inventive technology (ICSSIT) (pp. 460-465). IEEE.

[9] Pourhabibi, T., Ong, K. L., Kam, B. H., & Boo, Y. L. (2020). Fraud detection: A systematic literature review of graph-based anomaly detection approaches. Decision Support Systems, 133, 113303.

[10] Liu, Z., Dou, Y., Yu, P. S., Deng, Y., & Peng, H. (2020, July). Alleviating the inconsistency problem of applying graph neural network to fraud detection. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval (pp. 1569-1572).

[11] Cong, T. T. (2020). Credit card fraud detection based on machine learning. Tạp Chí Khoa học Trường Đại học Quốc tế Hồng Bàng, 45-52.

[12] Ding, Y., Kang, W., Feng, J., Peng, B., & Yang, A. (2023). Credit card fraud detection based on improved variational autoencoder generative adversarial network. Ieee Access, 11, 83680-83691.

[13] Najih, M. K. F. (2025). Forensic Auditing in Fraud Detection and Prevention: Integration of Technology, Internal Audit, and Anti-Fraud Regulation. Fairness, 1(1), 47-63.

[14] Sánchez-Aguayo, M., Urquiza-Aguiar, L., & Estrada-Jiménez, J. (2021). Fraud detection using the fraud triangle theory and data mining techniques: A literature review. Computers, 10(10), 121.

[15] Lebichot, B., Le Borgne, Y. A., He-Guelton, L., Oblé, F., & Bontempi, G. (2019, April). Deep-learning domain adaptation techniques for credit cards fraud detection. In INNS Big Data and Deep Learning conference (pp. 78-88). Cham: Springer International Publishing.

[16] Ashraf, M., Abourezka, M. A., & Maghraby, F. A. (2021). A comparative analysis of credit card fraud detection using machine learning and deep learning techniques. In Digital transformation technology: Proceedings of ITAF 2020 (pp. 267-282). Singapore: Springer Singapore.

[17] Srishailam, B., Rahul, Y., Pavan, P., Bharadwaj, Y., & Lokeshwar, K. (2024). Credit Card Fraud Detection Using Adaboost and Majority Voting: A Hybrid Approach for Real-Time Prevention. Macaw International Journal of Advanced Research in Computer Science and Engineering, 10(1s), 33-41.

[18] Ata, O., & Hazim, L. (2020). Comparative analysis of different distributions dataset by using data mining techniques on credit card fraud detection. Tehnički vjesnik, 27(2), 618-626.

[19] Dablain, D., Krawczyk, B., & Chawla, N. V. (2022). DeepSMOTE: Fusing deep learning and SMOTE for imbalanced data. IEEE transactions on neural networks and learning systems, 34(9), 6390-6404.

[20] Xu, Z., Shen, D., Nie, T., Kou, Y., Yin, N., & Han, X. (2021). A cluster-based oversampling algorithm combining SMOTE and k-means for imbalanced medical data. Information Sciences, 572, 574-589.

[21] Zhang, X., Li, P., Han, X., Yang, Y., & Cui, Y. (2024). Enhancing time series product demand forecasting with hybrid attention-based deep learning models. IEEE Access, 12, 190079-190091.

[22] Talaat, F. M., Aljadani, A., Badawy, M., & Elhosseini, M. (2024). Toward interpretable credit scoring: integrating explainable artificial intelligence with deep learning for credit card default prediction. Neural Computing and Applications, 36(9), 4847-4865.

[23] Forough, J., & Momtazi, S. (2022). Sequential credit card fraud detection: A joint deep neural network and probabilistic graphical model approach. Expert Systems, 39(1), e12795.

[24] Mineault, P. (2025). Is Attention All You Need?. In From Human Attention to Computational Attention: A Multidisciplinary Approach (pp. 297-314). Cham: Springer Nature Switzerland.

[25] Li, R., Liu, Z., Ma, Y., Yang, D., & Sun, S. (2022). Internet financial fraud detection based on graph learning. IEEE Transactions on Computational Social Systems, 10(3), 1394-1401.

[26] Dou, Y., Liu, Z., Sun, L., Deng, Y., Peng, H., & Yu, P. S. (2020, October). Enhancing graph neural network-based fraud detectors against camouflaged fraudsters. In Proceedings of the 29th ACM international conference on information & knowledge management (pp. 315-324).

[27] Zhu, D., Ma, Y., & Liu, Y. (2020, December). A flexible attentive temporal graph networks for anomaly detection in dynamic networks. In 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) (pp. 870-875). IEEE.

[28] Huang, X., Khetan, A., Cvitkovic, M., & Karnin, Z. (2020). Tabtransformer: Tabular data modeling using contextual embeddings. arXiv preprint arXiv:2012.06678.

[29] Gorishniy, Y., Rubachev, I., Khrulkov, V., & Babenko, A. (2021). Revisiting deep learning models for tabular data. Advances in neural information processing systems, 34, 18932-18943.

[30] Deng, T., Bi, S., & Xiao, J. (2024, December). Transformer-based financial fraud detection with cloud-optimized real-time streaming. In Proceedings of the 2024 5th International Conference on Big Data Economy and Information Management (pp. 702-707).

[31] Zhou, P., Lin, Q., Loghin, D., Ooi, B. C., Wu, Y., & Yu, H. (2021, April). Communication-efficient decentralized machine learning over heterogeneous networks. In 2021 IEEE 37th international conference on data engineering (ICDE) (pp. 384-395). IEEE.

[32] Zheng, W., Yan, L., Gou, C., & Wang, F. Y. (2021, January). Federated meta-learning for fraudulent credit card detection. In Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence (pp. 4654-4660).

[33] Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2), 1-19.

[34] Hueske, F., & Kalavri, V. (2019). Stream processing with Apache Flink: fundamentals, implementation, and operation of streaming applications. O'Reilly Media.

[35] Zhao, X., Sun, T., Ren, S., Yang, J., & Liu, Y. (2025). RAG-Based AI Agents for Enterprise Software Development: Implementation Patterns and Production Deployment. Frontiers in Artificial Intelligence Research, 2(3), 501-520.

[36] Theodorakis, G., Kounelis, F., Pietzuch, P. R., & Pirk, H. (2021). Scabbard: Single-Node Fault-Tolerant Stream Processing. Proc. VLDB Endow., 15(2), 361-374.

[37] Ballet, V., Renard, X., Aigrain, J., Laugel, T., Frossard, P., & Detyniecki, M. (2019). Imperceptible adversarial attacks on tabular data. arXiv preprint arXiv:1911.03274.

[38] Cartella, F., Anunciacao, O., Funabiki, Y., Yamaguchi, D., Akishita, T., & Elshocht, O. (2021). Adversarial attacks for tabular data: Application to fraud detection and imbalanced data. arXiv preprint arXiv:2101.08030.

[39] Li, X., Liu, K., Zhu, R., Kang, Y., Sun, C., Song, K., & Liu, X. (2022, December). Hierarchical Multi-task Learning for Enterprise Risk Detection from Financial Documents. In 2022 IEEE International Conference on Big Data (Big Data) (pp. 3505-3508). IEEE.

[40] Min, C., Liao, G., Wen, G., Li, Y., & Guo, X. (2023). Ensemble Interpretation: A Unified Method for Interpretable Machine Learning. arXiv preprint arXiv:2312.06255.

[41] Zhang, Y., Song, K., Sun, Y., Tan, S., & Udell, M. (2019). " Why should you trust my explanation?" Understanding uncertainty in LIME explanations. arXiv preprint arXiv:1904.12991.

[42] Zhang, Y., & Chen, X. (2020). Explainable recommendation: A survey and new perspectives. Foundations and Trends® in Information Retrieval, 14(1), 1-101.

[43] Guo, T., Lin, T., & Antulov-Fantulin, N. (2019, May). Exploring interpretable LSTM neural networks over multi-variable data. In International conference on machine learning (pp. 2494-2504). PMLR.

[44] Yang, J., Chen, K., Ding, K., Na, C., & Wang, M. (2023). Auto insurance fraud detection with multimodal learning. Data Intelligence, 5(2), 388-412.

[45] Krishna Rao, N. V., Harika Devi, Y., Shalini, N., Harika, A., Divyavani, V., & Mangathayaru, N. (2021). Credit card fraud detection using spark and machine learning techniques. In Machine Learning Technologies and Applications: Proceedings of ICACECS 2020 (pp. 163-172). Singapore: Springer Singapore.

[46] Afriyie, J. K., Tawiah, K., Pels, W. A., Addai-Henne, S., Dwamena, H. A., Owiredu, E. O., ... & Eshun, J. (2023). A supervised machine learning algorithm for detecting and predicting fraud in credit card transactions. Decision Analytics Journal, 6, 100163.

[47] Cheng, D., Wang, X., Zhang, Y., & Zhang, L. (2020). Graph neural network for fraud detection via spatial-temporal attention. IEEE Transactions on Knowledge and Data Engineering, 34(8), 3800-3813.

[48] Nalluri, M., Pentela, M., & Eluri, N. R. (2020). A scalable tree boosting system: XG boost. Int. J. Res. Stud. Sci. Eng. Technol, 7(12), 36-51.

[49] Dong, J., Jiang, Z., Pan, D., Chen, Z., Guan, Q., Zhang, H., ... & Gui, W. (2025). A survey on confidence calibration of deep learning-based classification models under class imbalance data. IEEE Transactions on Neural Networks and Learning Systems.

[50] Miao, J., & Zhu, W. (2022). Precision–recall curve (PRC) classification trees. Evolutionary intelligence, 15(3), 1545-1569.

[51] Jiang, T., Gradus, J. L., & Rosellini, A. J. (2020). Supervised machine learning: a brief primer. Behavior therapy, 51(5), 675-687.

[52] Sato, D. M. V., De Freitas, S. C., Barddal, J. P., & Scalabrin, E. E. (2021). A survey on concept drift in process mining. ACM Computing Surveys (CSUR), 54(9), 1-38.

[53] Sharvari, T., & Sowmya Nag, K. (2019). A study on modern messaging systems-kafka, rabbitmq and nats streaming. CoRR abs/1912.03715.

[54] Prathiba, B. (2026). Large-Scale Data Processing Using Distributed Computing Frameworks. International Journal of Applied Data Science & Modern Computing, 1(1), 27-39.

[55] Sahith, C. S. K., Muppidi, S., & Merugula, S. (2023, October). Apache spark big data analysis, performance tuning, and spark application optimization. In 2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT) (pp. 1-8). IEEE.

[56] Johnson, J. M., & Khoshgoftaar, T. M. (2019). Survey on deep learning with class imbalance. Journal of big data, 6(1), 27.

[57] Hajihosseinlou, M., Maghsoudi, A., & Ghezelbash, R. (2023). A novel scheme for mapping of MVT-type Pb–Zn prospectivity: LightGBM, a highly efficient gradient boosting decision tree machine learning algorithm. Natural resources research, 32(6), 2417-2438.

[58] He, J., Mao, R., Shao, Z., & Zhu, F. (2020). Incremental learning in online scenario. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 13926-13935).

[59] Tao, Z., & Chao, J. (2023, October). Financial fraud and anomaly detection techniques: A literature review. In Proceedings of the 2023 4th International Conference on Computer Science and Management Technology (pp. 634-641).

[60] Allen, J. (2025). CardSim: A Bayesian simulator for payment card fraud detection research.

[61] Anderson, R., Koh, Y. S., Dobbie, G., & Bifet, A. (2019). Recurring concept meta-learning for evolving data streams. Expert Systems with Applications, 138, 112832.

[62] Balayn, A., Lofi, C., & Houben, G. J. (2021). Managing bias and unfairness in data for decision support: a survey of machine learning and data engineering approaches to identify and mitigate bias and unfairness within data management and analytics systems. The VLDB Journal, 30(5), 739-768.

[63] Song, Z., Zhang, Y., & King, I. (2023, October). Towards fair financial services for all: A temporal GNN approach for individual fairness on transaction networks. In Proceedings of the 32nd ACM international conference on information and knowledge management (pp. 2331-2341).