AI-Powered Payment Risk Scoring on Cloud-Native Big Data Systems
Keywords:
Payment Risk Scoring Systems, Cloud-Native Big Data Architecture, Real-Time Risk Evaluation, Payment Service Providers, Financial Transaction Analytics, Machine Learning–Based Risk Metrics, Near-Native Response Time, Data Generation And Ingestion, Event-Driven Processing, Batch Data Processing, Cloud-Native IoT Systems, Risk Scoring Services, Lifelong Machine Learning, CI/CD Pipelines For ML, Data Governance And Privacy, Zero-Trust Security Architecture, Observability And Monitoring, Scalable Financial Platforms, Payment Analytics Data Lakes, Intelligent Financial Risk Management.Abstract
The growing volumes of payment transactions have placed significant burden on payment service providers or financial institutions to evaluate the risk profiles of customers swiftly. Various risk-related metrics of customers can be obtained from the data related to their payment transactions, and the recent development of machine learning-based modeling approaches allows these metrics to be modeled as a risk score. However, the shoe has on the other foot as providing such services has become a competitive game between players leading to the demand for near-native response time in serving customer requests. The services, therefore, require a cloud-native big data architecture where the data generation, ingestion, processing and modeling operations can be carried out at huge scales and low response and lead times while integrating the required principles of data governance and privacy. Cloud-native IoT systems collect vast volumes of data daily that forms the data lake for processing. Data processing includes both batch and event-driven processing. Batch processing is used for generating risk-related metrics where as event-driven processing is used for carrying out risk scoring. The ML models behind risk metrics are provided as services and lifelong learning is done by using CI/CD pipelines. Firewall and observability adopt a zero-trust policy to promote not just security but also privacy and governance compliance. Robust architecture has enormous readability and scalability features for tackling such diverse services. The entire architecture is illustrated with an example in the domain of payment service providers using a risk scoring system proposed in the previous work.
References
1. Abdallah, A., Maarof, M. A., & Zainal, A. (2020). Fraud detection system: A survey. Journal of Network and Computer Applications, 150, 102452.
2. Ahmed, M., Mahmood, A. N., & Hu, J. (2020). A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60, 19–31.
3. Alghushairy, O., Alsini, R., Soule, T., & Ma, X. (2021). A review of local outlier factor algorithms for outlier detection in big data streams. Big Data and Cognitive Computing, 5(1), 1–24.
4. Bandi, V. D. V. K. (2024). AI-Driven Predictive Risk Modeling Architectures for Financial Systems. International Journal Of Finance, 37(3), 54-78.
5. Bahnsen, A. C., Aouada, D., Ottersten, B., & Stojanovic, A. (2020). Feature engineering strategies for credit card fraud detection. Expert Systems with Applications, 151, 113361.
6. Bhatla, T., Prabhu, V., & Dua, M. (2021). Credit card fraud detection using machine learning: A systematic literature review.SNComputerScience, 2(6), 469.
7. Brintrup, A., Pak, J., Pearson, S., & Ali, M. (2022). Machine learning for financial risk management: A review. IEEE Access, 10, 11539–11558.
8. 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.
9. Chandola, V., Banerjee, A., & Kumar, V. (2020). Anomaly detection for financial transaction systems: A survey. ACM Computing Surveys, 53(3), 1–37.
10. Inala, R. Advancing Group Insurance Solutions Through Ai-Enhanced Technology Architectures And Big Data Insights.
11. Chen, J., Li, X., Zhang, H., & Wang, S. (2024). Artificial intelligence for financial fraud detection: Recent advances and future directions. Expert Systems with Applications, 237, 121451.
12. Cui, L., Xie, X., & Zhu, Y. (2023). Intelligent payment fraud detection using graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 34(11), 8732–8745.
13. Dal Pozzolo, A., Caelen, O., Johnson, R. A., & Bontempi, G. (2020). Adaptive machine learning for fraud detection in payment systems. Data Mining and Knowledge Discovery, 34(2), 391–419.
14. Pamisetty, V., & Amistapuram, K. Smart Decision Support Systems For Dynamic Tax Policy Optimization Using Reinforcement Learning.
15. Dua, D., Graff, C., & Karra Taniskidou, E. (2021). Big data analytics for financial fraud detection using cloud computing. Future Generation Computer Systems, 118, 323–336.
16. Eling, M., & Lehmann, M. (2022). The impact of digitalization and artificial intelligence on financial services. Geneva Papers on Risk and Insurance, 47(3), 405–430.
17. Fiore, U., De Santis, A., Perla, F., Zanetti, P., & Palmieri, F. (2020). Using generative adversarial networks for fraud detection. Information Sciences, 479, 448–455.
18. Ghosh, S., & Reilly, D. (2021). Credit card fraud detection with machine learning: A review. Journal of Financial Crime, 28(2), 465–486.
19. Mangalampalli, B. M. (2024). Transparent Intelligence Explainability Frameworks for AI-Driven Clinical Decision Support in Healthcare Business Intelligence. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(3), 10566-10579.
20. Goyal, M., Gupta, A., & Kumar, P. (2022). Artificial intelligence in financial risk management: A comprehensive review. Journal of Risk and Financial Management, 15(7), 301.
21. Hajek, P., & Henriques, R. (2023). Explainable artificial intelligence in financial risk prediction. Expert Systems with Applications, 213, 118938.
22. Hasan, M., Islam, M. M., Zarif, M. I. I., & Hashem, M. M. A. (2021). Attack and anomaly detection in cloud computing using machine learning. IEEE Access, 9, 129499–129514.
23. Kolla, S. K. (2022). Engineering Healthcare Data Infrastructures for Predictive Clinical Analytics and Evidence-Based Decision Making. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(5), 5370-5380.
24. Jurgovsky, J., Granitzer, M., Ziegler, K., Calabretto, S., Portier, P. E., He-Guelton, L., & Caelen, O. (2020). Sequence classification for credit-card fraud detection. Expert Systems with Applications, 100, 234–245.
25. Kou, G., Chao, X., Peng, Y., Alsaadi, F. E., & Herrera-Viedma, E. (2021). Machine learning methods for systemic financial risk analysis. Decision Support Systems, 142, 113473.
26. Le Borgne, Y. A., Siblini, W., Lebichot, B., & Bontempi, G. (2022). Reproducible machine learning for credit card fraud detection. Journal of Big Data, 9(1), 1–27.
27. Li, J., Zhao, Y., & Zhang, L. (2024). Cloud-native artificial intelligence architecture for financial transaction risk analytics. Future Internet, 16(3), 92.
28. Li, X., Ding, Y., & Wang, H. (2023). Deep learning methods for fraud detection in electronic payment systems. IEEE Access, 11, 112980–112995.
29. Liu, Y., Chen, H., & Xu, Z. (2021). Big data analytics for intelligent financial risk management. Journal of Ambient Intelligence and Humanized Computing, 12(9), 8619–8632.
30. Lucas, Y., & Jurgovsky, J. (2020). Credit card fraud detection using machine learning: A survey.
31. Maiti, M., Vyklyuk, Y., & Vasa, L. (2022). Machine learning in banking risk management: Applications and challenges. Risks, 10(8), 150.
32. Makki, S., Assaghir, Z., Taher, Y., Haque, R., Hacid, M. S., & Zeineddine, H. (2021). An experimental study with imbalanced classification approaches for fraud detection. IEEE Access, 9, 9309–9322.
33. Namiot, D., & Sneps-Sneppe, M. (2020). Cloud-native applications for big data processing. Programming and Computer Software, 46(6), 401–408.
34. Mangala, N. (2024). Leveraging Microsoft Fabric lakehouse as an AI-ready data platform for enterprise analytics. Journal of Information Systems Engineering and Management.
35. Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2020). The application of data mining techniques in financial fraud detection: A classification framework and literature review. Decision Support Systems, 50(3), 559–569.
36. Ospanova, A., & Nurlybekova, G. (2023). Artificial intelligence in fintech risk assessment: A systematic review. Journal of Open Innovation: Technology, Market, and Complexity, 9(4), 100146.
37. Kolla, S. H. (2024). Retrieval-Augmented Enterprise Intelligence: Enhancing Accuracy, Trust, and Operational Decision-Making. International Journal of Future Innovative Science and Technology (IJFIST), 7(2), 12425.
38. Panigrahi, S., Kundu, A., Sural, S., & Majumdar, A. K. (2021). Credit card fraud detection: A fusion approach using Dempster–Shafer theory and Bayesian learning. Information Fusion, 73, 45–58.
39. Pham, Q. V., Fang, F., Ha, V. N., Piran, M. J., Le, M., Le, L. B., & Hwang, W. J. (2020). A survey of multi-access edge computing in 5G and beyond. IEEE Access, 8, 116974–117017.
40. Priyadarshini, I., Son, L. H., Abdel-Basset, M., Elhoseny, M., & Long, H. V. (2022). Intelligent fraud detection in financial services using deep learning. Computers & Security, 114, 102589.
41. Rane, N. (2024). Artificial intelligence, machine learning, and big data analytics in fintech. Journal of Risk and Financial Management, 17(2), 67.
42. Mukesh, A., & Aitha, A. R. (2021). Insurance Risk Assessment Using Predictive Modeling Techniques. International Journal of Emerging Research in Engineering and Technology, 2(4), 68-79.
43. Roy, A., Sun, J., Mahoney, R., Alonzi, L., Adams, S., & Beling, P. (2021). Deep learning detecting fraud in financial transactions. Machine Learning with Applications, 5, 100037.
44. Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3), 160.
45. Sharma, A., & Kumar, R. (2023). AI-enabled financial fraud analytics in cloud environments. IEEE Access, 11, 90432–90450.
46. Singh, A., & Sharma, P. (2022). Big data technologies for scalable financial analytics. Journal of Big Data, 9(1), 65.
47. Soni, N., Sharma, E. K., Singh, N., & Kapoor, A. (2021). Artificial intelligence in business: From research to deployment. International Journal of Information Management Data Insights, 1(2), 100040.
48. Sun, Y., Zhang, C., & Wang, J. (2024). Explainable AI models for payment transaction risk scoring. Expert Systems with Applications, 245, 123185.
49. Tigges, M., Mestwerdt, S., Tschirner, S., & Mauer, R. (2024). Who gets the money? A qualitative analysis of fintech lending and credit scoring through the adoption of AI and alternative data. Technological Forecasting and Social Change, 205, 123491.
50. Vassallo, D., Vella, V., & Azzopardi, G. (2023). Real-time fraud detection using streaming analytics and cloud-native architectures. Future Generation Computer Systems, 143, 110–123.
51. Wickramanayake, B., Geeganage, D. K., Ouyang, C., & Xu, Y. (2020). A survey of online card payment fraud detection using data mining-based methods.
52. Yandamuri, U. S. (2024). AI-Driven Decision Support Systems for Operational Optimization in Hospitality Technology. Metallurgical and Materials Engineering.
53. Wu, Y., Lin, Z., & Liu, J. (2023). Intelligent payment risk prediction with ensemble learning. Knowledge-Based Systems, 269, 110510.
54. Xie, L., Chen, X., & Zhou, Y. (2022). Financial transaction anomaly detection using graph representation learning. IEEE Access, 10, 122456–122469.
55. Xu, H., Zhang, S., & Li, M. (2024). Cloud-native machine learning pipelines for scalable financial analytics. Future Generation Computer Systems, 153, 178–191.
56. Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2020). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology, 10(2), 1–19.
57. Yin, C., Zhu, Y., Fei, J., & He, X. (2021). A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access, 9, 21954–21961.
58. Mattaparthi, R. (2022). Engineering Predictive Industrial Systems Through IoT-Driven Asset Monitoring and Machine Learning Prognostics. International Journal of Future Innovative Science and Technology (IJFIST), 5(1), 7790.
59. Yu, H., Zhao, X., & Li, Z. (2023). AI-driven financial transaction monitoring using cloud computing. Journal of Cloud Computing, 12(1), 88.
60. Zeng, Y., Wang, F., & Zhao, H. (2022). Big data analytics for financial payment security. Information Systems Frontiers, 24(5), 1459–1474.
61. Loganathan, R. (2021). Integrated Risk and Compliance Frameworks for Global Data Center Operations: A Governance-Centric Approach. Universal Journal of Computer Sciences and Communications, 1(1), 1-26.
62. Zhang, H., Wang, Y., & Chen, L. (2024). Transformer-based payment fraud detection in cloud-native financial systems. Expert Systems with Applications, 246, 123504.
63. Zhang, J., & Chen, X. (2021). Artificial intelligence for financial services: Applications and challenges. Financial Innovation, 7(1), 45.
64. Zhang, K., Li, W., & Xu, P. (2023). Explainable machine learning for financial transaction risk assessment. Knowledge-Based Systems, 267, 110395.
65. Davuluri, P. N. (2019). Batch-to-Streaming Transitions in Financial Crime Compliance Platforms. International Journal Of Engineering And Computer Science, 8(12).
66. Zhang, Y., Liu, H., & Sun, P. (2022). Real-time payment fraud detection with streaming big data analytics. IEEE Access, 10, 115284–115297.
67. Zhao, L., Chen, S., & Wang, Y. (2024). Large-scale payment risk scoring using graph neural networks and cloud computing. Information Sciences, 671, 120703.
68. Zhou, J., Li, X., & Wang, T. (2023). AI-powered anomaly detection in digital payment ecosystems. Applied Soft Computing, 141, 110324.
69. Zhou, Y., Wu, J., & Xu, F. (2022). Scalable cloud-native data platforms for financial intelligence. Future Internet, 14(10), 297.
70. Reddy Segireddy, A. (2024). Federated Cloud Approaches for Multi-Regional Payment Messaging Systems. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(2), 442-450.
71. Ziegler, K., Jurgovsky, J., Granitzer, M., & Calabretto, S. (2021). Sequential deep learning models for payment fraud detection. Expert Systems with Applications, 185, 115562.
72. Zięba, M., Tomczak, J. M., Lubicz, M., & Świątek, J. (2021). Boosted support vector machines for fraud detection in financial transactions. Journal of Artificial Intelligence Research, 70, 105–131.
73. Kolla, T. (2024). Graph Neural Networks for HCC Risk Adjustment and Interoperability. International Journal of Science, Research and Technology, 7(6), 13244-13255.
74. Zimmermann, H., Fischer, S., & Müller, T. (2024). Cloud-native big data architectures for AI-powered financial risk scoring and fraud prevention. Journal of Big Data, 11(1), 84.
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