A Smart Approach to Financial Crime Defense
Keywords:
Cross-Border Payments, Real-Time Payment Processing, Financial Data Engineering, Cloud-Native Payment Architecture, Streaming Payment Pipelines, Payment Data Integration, Low-Value High-Frequency Transactions, Anti-Money Laundering (AML), Counter-Terrorism Financing (CTF), Real-Time Compliance Monitoring, AI-Driven Financial Intelligence, Payment Risk Analytics, Transaction Screening Systems, Financial Regulatory Compliance, Payment Routing Optimization, Data Hub Architecture, DevOps for Financial Systems, Latency-Critical Systems, Digital Remittance Platforms, Intelligent Payment Ecosystems.Abstract
Advances in modern communications and digital services have reshaped the cross-border payments landscape, making low-value, high-frequency transactions increasingly attractive while raising user expectations. At the same time, the growing number of remittance providers has fueled demand for disruptive innovations—real-time processing, lower transaction costs, greater visibility, and, critically, stronger mitigation of existing risks. Financial regulators, meanwhile, view these same low-value, high-frequency transactions as a key vector for money laundering and terrorism financing, requiring all institutions involved in cross-border traffic to build data intelligence capabilities capable of monitoring risk in real time.
To meet these regulatory demands, this work proposes a cloud-native financial data-engineering framework for real-time cross-border payments. The framework combines a common data hub for ingestion and integration, streaming pipelines dedicated to payment transactions, and AI-driven intelligence capabilities that support robust processing alongside latency-critical, interactive applications. Shared infrastructure and DevOps practices are built into the design. The framework is operationalized through two services: real-time compliance screening and cross-border payment routing.
References
1. Lucas, Y., & Jurgovsky, J. (2020). Credit card fraud detection using machine learning: A survey. arXiv.
2. Enhanced credit card fraud detection based on SVM-recursive feature elimination and hyper-parameters optimization. (2020). Journal of Information Security and Applications, 55, Article 102596.
3. Hilal, W., Gadsden, S. A., & Yawney, J. (2022). Financial fraud: A review of anomaly detection techniques and recent advances. Expert Systems with Applications, 193, Article 116429.
4. Kumar, S. S., Gadi, A. L., Sheelam, G. K., Kummari, D. N., Reddy Koppolu, H. K., & Pamisetty, A. (2026). AI-Driven Compliance and Audit Framework for Manufacturing Infrastructure in Automotive Connected Services and Financial Ecosystems. In 2026 IEEE International Conference for Convergence in Computing Technology (I3CTCON) (pp. 1–6). IEEE. 2026 IEEE International Conference for Convergence in Computing Technology (I3CTCON). https://doi.org/10.1109/i3ctcon68242.2026.11507231
5. Bin Sulaiman, R., Schetinin, V., & Sant, P. (2022). Review of machine learning approach on credit card fraud detection. Human-Centric Intelligent Systems, 2, 55–68.
6. Ileberi, E., Sun, Y., & Wang, Z. (2022). A machine learning based credit card fraud detection using the GA algorithm for feature selection. Journal of Big Data, 9, Article 24.
7. Roseline, F. J., Naidu, G. B. S. R., Pandi, V. S., Mageswari, N., & other coauthors. (2022). Autonomous credit card fraud detection using machine learning approach. Computers and Electrical Engineering, 102, Article 108132.
8. Nandan, B. P., Kumar, M. V. K., Garapati, R. S., Bandi, V. D. V. K., Davuluri, P. S. L. N., & Mangalampalli, B. M. (2026). AI-Enhanced Semiconductor Yield Optimization Using Hybrid Deep Learning and Edge Data Analytics. In 2026 IEEE International Conference on AI Engineering and Innovations (AIEI) (pp. 1–6). IEEE. 2026 IEEE International Conference on AI Engineering and Innovations (AIEI). https://doi.org/10.1109/aiei69164.2026.11497190
9. Credit card fraud detection in the era of disruptive technologies: A systematic review. (2023). Journal of King Saud University—Computer and Information Sciences, 35(1), 145–174.
10. Afriyie, J. K., Tawiah, K., Pels, W. A., Addai-Henne, S., Dwamena, H. A., Owiredu, E. O., Ayeh, S. A., & Eshun, J. (2023). A supervised machine learning algorithm for detecting and predicting fraud in credit card transactions. Decision Analytics Journal, 6, Article 100163.
11. Krishnan, M., Yandamuri, U. S., Recharla, M., Challa, K., Kolla, S. H., & Valiki, S. (2026). Adaptive Machine Learning Ecosystem for Personalized Medicine and Digital Finance: Leveraging Azure Cloud, Data Engineering, and Workflow Automation. In 2026 5th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0 (pp. 1–6). IEEE. 2026 5th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0. https://doi.org/10.1109/otcon69332.2026.11629728
12. Tong, G., & Shen, J. (2023). Financial transaction fraud detector based on imbalance learning and graph neural network. Applied Soft Computing, 149, Article 110984.
13. Motie, S., & Raahemi, B. (2024). Financial fraud detection using graph neural networks: A systematic review. Expert Systems with Applications, 240, Article 122156.
14. Sudha Rani, P. R., Amistapuram, K., Pamisetty, V., Singireddy, S., Kummari, D. N., & Sheelam, G. K. (2025). Hybrid Knowledge Graph–Deep Learning Framework for Automated Exception Handling and Investigation in Complex Insurance Claims. In 2025 IEEE 3rd Global Conference on Wireless Computing and Networking (GCWCN) (pp. 1–6). IEEE. 2025 IEEE 3rd Global Conference on Wireless Computing and Networking (GCWCN). https://doi.org/10.1109/gcwcn66157.2025.11448301
15. Hernandez Aros, L., Bustamante Molano, L. X., Gutierrez-Portela, F., Moreno Hernandez, J. J., & other coauthors. (2024). Financial fraud detection through the application of machine learning techniques: A literature review. Humanities and Social Sciences Communications, 11, Article 1130.
16. Chen, C.-T., Lee, C., Huang, S.-H., & Peng, W.-C. (2024). Credit card fraud detection via intelligent sampling and self-supervised learning. ACM Transactions on Intelligent Systems and Technology, 15(2), Article 35.
17. Kolla, S. K., Bandi, V. D. V. K., & Meda, R. (2026). Comment on “From laboratory promise to decision-grade practice: strengthening reproducibility and casework relevance in AI-assisted bloodstain ageing.” Forensic Science, Medicine and Pathology. https://doi.org/10.1007/s12024-026-01258-x
18. Salam, M. A., Fouad, K. M., Elbably, D. L., & other coauthors. (2024). Federated learning model for credit card fraud detection with data balancing techniques. Neural Computing and Applications, 36, 6231–6256.
19. Aslam, A., & Hussain, A. (2024). A performance analysis of machine learning techniques for credit card fraud detection. Journal on Artificial Intelligence, 6(1), 1–21.
20. Yandamuri, U. S. Next-Generation Industry Platforms Integrated Systems For Predictive & Intelligent Operations. Solaris Vantage.
21. Deep learning for credit card fraud detection: A review of algorithms, challenges, and solutions. (2024). IEEE Access, 12, 96893–96910.
22. Cheng, D., Zou, Y., Xiang, S., & Jiang, C. (2025). Graph neural networks for financial fraud detection: A review. Frontiers of Computer Science, 19(9), Article 199609.
23. Raut, B., Garg, A., Mangalampalli, B. M., Jindal, N., Mahajan, V., & Saraswati, K. (2026). Multidisciplinary Strategies for Improving Maternal and Reproductive Health Outcomes and Mental Outcomes. International Journal of Drug Delivery Technology, 16(34s). https://doi.org/10.25258/ijddt.16.34s.36
24. Intelligent anti-money laundering fraud control using graph-based machine learning model for the financial domain. (2023). Journal of Cases on Information Technology, 25(1).
25. Artificial intelligence for enhanced anti-money laundering and asset recovery: A new frontier in financial crime prevention. (2024). In Proceedings of the International Conference on Intelligent and Innovative Computing Applications.
26. Meda, R., Motamary, S., Dodda, A., Kummari, D. N., Kaulwar, P. K., & Gadi, A. L. (2026). Edge-AI Integration for Accelerated Decision-Making in Industrial Robotics and Manufacturing Plants. In Lecture Notes in Networks and Systems (pp. 332–344). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-19185-4_27
27. Financial fraud detection using improved deep learning models. (2024). Computers, Materials & Continua, 78(1), 1049–1069.
28. Credit card fraud detection using machine learning. (2024). In Proceedings of the International Conference on Advancements in Smart, Secure and Intelligent Computing.
29. A machine learning method with hybrid feature selection for improved credit card fraud detection. (2023). Applied Sciences.
30. Mahadevan, S. (2026). Governed Serverless Automation Ecosystem for AI-Driven Enterprise Integration and Sustainable Cloud Operations. International Journal of Computer Information Systems and Industrial Management Applications, 18(16s), 1561-1570.
31. Ensemble synthesized minority oversampling-based generative adversarial networks and random forest algorithm for credit card fraud detection. (2023). IEEE Access.
32. Credit card fraud detection through intelligent sampling and self-supervised learning. (2024). ACM Transactions on Intelligent Systems and Technology, 15(2).
33. Financial fraud detection through the application of machine learning techniques: A literature review. (2024). Humanities and Social Sciences Communications, 11, Article 1130.
34. Graph learning-empowered financial fraud detection: Progress and future directions. (2025).
35. Deep learning in financial fraud detection: Innovations, challenges, and applications. (2025).
36. Engineering Intelligent Cloud-Native Data Ecosystems for Predictive Decision-Making in Industry. (2026). Journal of European Economic History, 7(2), 68–88. https://doi.org/10.61336/jeeh/26-2-7 (Original work published as Engineering Intelligent Cloud-Native Data Ecosystems for Predictive Decision-Making in Industry)
37. A survey of machine learning and deep learning for financial fraud detection: Architectures, data modalities, and real-world deployment challenges. (2026).
38. Financial fraud detection using graph neural networks: A systematic review. (2024). Expert Systems with Applications, 240, Article 122156.
39. Credit card fraud detection in the era of disruptive technologies: A systematic review. (2023). Journal of King Saud University—Computer and Information Sciences, 35(1), 145–174.
40. Financial transaction fraud detector based on imbalance learning and graph neural network. (2023). Applied Soft Computing, 149, Article 110984.
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Copyright (c) 2026 Someshwar Mashetty, Alexander Petrov (Author)
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