Streaming Sanctions Screening with Budget-Aware Data Governance in the Cloud
Keywords:
Streaming Sanctions Intelligence, Event-Driven Compliance, Cloud Data Governance, Financial Sanctions Compliance, Cost-Aware Cloud Governance, Regulatory Compliance, Event-Driven Architecture (EDA), Financial Crime Risk Management, Sensitive Data Protection, Compliance Intelligence.Abstract
This work addresses two interrelated developments at the intersection of finance, cloud data governance, and compliance.
First, financial sanctions regimes require sanctions intelligence—current information on named sanctions targets, including aliases, applicable sanctions lists, and related metadata. Given the rapidly expanding number of sanctioned entities, the frequent updates to sanctions lists, and the significant risk of engaging with sanctioned parties, keeping this intelligence current, ideally in real time, is essential. We introduce the concept of streaming sanctions intelligence to capture this requirement.
Second, the growing use of cloud platforms to store and process sensitive data—data subject to regulatory, contractual, or policy obligations—has raised concerns about the adequacy of existing cloud data governance practices. We introduce the concept of cost-aware cloud data governance, which addresses two complementary needs: making analytical data available to satisfy policy obligations while minimising cloud-related costs, and protecting sensitive data against excessive exposure.
Building on these two concepts, we present an architectural instantiation in the form of an event-driven financial compliance fabric, designed to ingest and process streams of sanctions intelligence while enabling cost-aware governance of sensitive data in the cloud. The architecture applies event-driven architecture principles to support compliance workflows that respond to events both within and outside the organisation. Streaming signals concerning named sanctions targets and their associated metadata are defined, semantically enriched, and recorded in a live feed for use by organisational monitoring processes. By combining these capabilities, the proposed compliance fabric strengthens data governance, reduces the operational risk of sanctions non-compliance, and demonstrates how cloud scalability and compliance can be reconciled by minimising the retention of data evidence in the cloud.
References
1. Alarab, I., Prakoonwit, S., & Nacer, M. I. (2020). Comparative analysis using supervised learning methods for anti-money laundering in Bitcoin. In Proceedings of the 2020 5th International Conference on Machine Learning Technologies (pp. 60–66).
2. Jullum, M., Løland, A., Huseby, R. B., Ånonsen, G., & Sudjianto, A. (2020). Detecting money laundering transactions with machine learning. Journal of Money Laundering Control, 23(1), 173–186.
3. Kim, S. K., & Yang, S. Y. (2024). Accuracy improvement in financial sanction screening: Is natural language processing the solution? Frontiers in Artificial Intelligence, 7.
4. Bhandari, P., Prithviraj, J., Saxena, R., Kumar, D., Mangala, N., & Chourasiya, P. (2026). Harris Hawks Optimization–Based Deep Learning for Resource-Aware Cloud–Fog Computing. 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/otcon69332.2026.11629982
5. Grzegorowski, M., Zdravevski, E., Janusz, A., Lameski, P., Apanowicz, C., & Ślęzak, D. (2021). Cost optimization for big data workloads based on dynamic scheduling and cluster-size tuning. Big Data Research, 25, Article 100203.
6. Han, J., & Chun, S. (2025). Budget-constrained runtime allocation of linked data services in stream processing. Data Science and Engineering, 10, 277–295.
7. Seenu, A., Kolla, S. H., Aitha, A. R., Amistapuram, K., Abhireddy, N., & Yandamuri, U. S. (2026, June). Agentic Enterprise Intelligence: A Unified Architecture for Secure RAG-Enabled LLM Automation in Insurance Risk and Fraud Ecosystems. In 2026 5th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0 (pp. 1-5). IEEE.
8. Mirza, N., Elhoseny, M., Umar, M., & Metawa, N. (2023). Safeguarding FinTech innovations with machine learning: Comparative assessment of various approaches. Research in International Business and Finance, 66, Article 102009.
9. A cost-aware operator migration approach for distributed stream processing system. (2025). IEEE Transactions on Cloud Computing, 13(1), 441–454.
10. Avinash, K., Ranga Reddy, V. A., Manohari, D., Kothuri, S., Geetha, R., & Swarna, B. (2026). Multi-Source Feature Integration for Short-Term Load Forecasting in Off-Grid Residential Microgrids Using Stacked LSTM Networks. In 2026 14th International Conference on Smart Grid (icSmartGrid) (pp. 501–505). IEEE. 2026 14th International Conference on Smart Grid (icSmartGrid). https://doi.org/10.1109/icsmartgrid70882.2026.11680767
11. To store or not: Online cost optimization for running big data jobs on the cloud. (2024). Future Generation Computer Systems, 156, 42–52.
12. Throughput optimization for Storm-based processing of stream data on clouds. (2020). Future Generation Computer Systems, 112, 567–579.
13. 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)
14. Resource management and scheduling in distributed stream processing systems: A taxonomy, review, and future directions. (2020). ACM Computing Surveys, 53(3).
15. Cloud cost optimization: Model, bounds, and asymptotics. (2023). Operations Research.
16. Kurum, E. (2023). RegTech solutions and AML compliance: What future for financial crime? Journal of Financial Crime, 30(3), 776–794.
17. Singh, K. U., Gupta, S. K., Dubey, Y., & Mangalampalli, B. M. (2026, June). Enhancing Healthcare Cyber Defense with Machine Learning-Based Attack Prediction and Prevention. In 2026 5th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0 (pp. 1-6). IEEE.
18. Naaz, J., Patel, H., & Syed, Z. (2026). Cloud-native event-driven orchestration architecture for real-time fraud and sanctions screening. American International Journal of Computer Science and Technology.
19. Sathiri, D. (2023). Transparent alert reduction for streaming sanctions compliance. American Advanced Journal for Emerging Disciplinaries.
20. 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.
21. Abhireddy, N. (2023). AI-powered real-time sanctions screening for cloud-native finance. The American Online Journal of Science and Engineering.
22. Mishra, M. K. (2026). Modernizing anti-money laundering (AML) data pipelines using cloud-native architectures on Azure and Databricks. International Journal of AI, BigData, Computational and Management Studies.
23. Loganathan, R. (2026). SaaS Entitlement Governance: A Reproducible Model for Detecting and Reclaiming Over-Provisioned Access at Enterprise Scale. Journal of Intelligent Decision Making and Information Science, 3(7s), 2519-2530.
24. Liu, C., Tang, H., Yang, Z., Zhou, K., & Cha, S. (2025). Big data-driven fraud detection using machine learning and real-time stream processing. arXiv.
25. Bonthala, D. (2025). Telemetry driven cost governance for enterprise data and AI platforms. Journal of Information Systems Engineering and Management, 10(9s).
26. Rani, S., Thavara, S. S., Kr, A., Naguband, A. R., Garg, A., & Vajpayee, A. Financialization of Sustainability: ESG Signalling, Market Valuation and the New Economics of Corporate Legitimacy.
27. Yandamuri, U. S. (2022). Cloud-based data integration architectures for scalable enterprise analytics. International Journal of Intelligent Systems and Applications in Engineering, 10, 472–483.
28. Cost-aware operator migration approach for distributed stream processing systems. (2025). IEEE Transactions on Cloud Computing.
29. 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
30. Apache Software Foundation. (2020). Apache Kafka documentation.
31. Apache Software Foundation. (2021). Apache Flink documentation.
32. Gorle, R. (2026). Adaptive by Design: Building Data Infrastructure That Rewrites Itself at Scale. Apollo Publication.
33. National Institute of Standards and Technology. (2020). Security and privacy controls for information systems and organizations (NIST Special Publication 800-53, Revision 5).
34. National Institute of Standards and Technology. (2020). Zero trust architecture (NIST Special Publication 800-207).
35. Anand, V., A, S. K., Vemulapalli, N., Loganathan, R., Guntupalli, A., & Ahammed, S. R. (2026). Federated Deep Learning for Decentralized Cybersecurity in Smart Grid Communication Networks. In 2026 14th International Conference on Smart Grid (icSmartGrid) (pp. 475–481). IEEE. 2026 14th International Conference on Smart Grid (icSmartGrid). https://doi.org/10.1109/icsmartgrid70882.2026.11680752
36. Financial Action Task Force. (2020). Guidance on digital identity.
37. Kolla, S. K., Bandi, V. D. V. K., & Meda, R. (2026). Comment on “Predicting self-image satisfaction after adult spinal deformity surgery: a machine learning approach using patient phenotypes.” Spine Deformity. https://doi.org/10.1007/s43390-026-01400-3
38. Financial Action Task Force. (2021). Opportunities and challenges of new technologies for AML/CFT.
39. Financial Action Task Force. (2021). Updated guidance for a risk-based approach to virtual assets and virtual asset service providers.
40. Babu, A. J., Yandamuri, U. S., Recharla, M., Pamisetty, A., Goli, M., & Kolla, S. H. (2026, June). Data Analytics and AI Solutions for Digital Transformation in Hospitality and AgriTech Industries. In 2026 5th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0 (pp. 1-7). IEEE.
41. Financial Action Task Force. (2022). International standards on combating money laundering and the financing of terrorism and proliferation: The FATF recommendations.
42. Gorle, R., & Pareek, P. K. (2026). Agentic AI-Based Security Orchestration for Oracle Cloud and Multi-Cloud Platforms. International Journal of Computer Information Systems & Industrial Management Applications, 18, 354.
43. Financial Action Task Force. (2023). Opportunities and challenges of new technologies for AML/CFT.
44. Financial Action Task Force. (2024). Guidance on beneficial ownership of legal persons.
45. Office of Foreign Assets Control. (2021). Sanctions compliance guidance for the virtual currency industry.
46. Basel Committee on Banking Supervision. (2021). Principles for the effective management and supervision of climate-related financial risks.
47. Nemani, R., Singuluri, I., Ramani, V. S., Guntupalli, A., Mopathi, U., & Madineni, A. V. S. (2026, July). A Machine Learning-Augmented Penalty Difference Approach for Transportation Cost Optimization. In 2026 14th International Conference on Smart Grid (icSmartGrid) (pp. 511-516). IEEE.
48. European Banking Authority. (2021). Guidelines on money laundering and terrorist financing risk factors.
49. European Banking Authority. (2022). Guidelines on the use of remote customer onboarding solutions.
50. International Organization for Standardization. (2022). Information security, cybersecurity and privacy protection—Information security management systems—Requirements (ISO/IEC 27001:2022).
Additional Files
Published
Data Availability Statement
None
Issue
Section
License
Copyright (c) 2026 Venkata krishna azith ganti, Luca Romano (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
