Event-Native Platforms for Standardizing Processes and Proving Compliance
DOI:
https://doi.org/10.5281/06q4wg79Keywords:
Intelligent event-driven platforms; enterprise finance; compliance; risk management; business process harmonization; operational data; event stream architecture; event-centric analytics; event-driven architecture; auditability; control points; process mapping; data-binding; data-sharing; data-classification; metadata-management; role-based-access-control; workflow-operations-control.Abstract
Enterprise systems generate events every millisecond, yet most finance processes fail to keep pace. Rather than fully leveraging existing IT infrastructure, financial operations still rely heavily on batch processing. This creates a lag in internal resource allocation and limits the enterprise's ability to act proactively. By contrast, other parts of the enterprise continuously monitor capability-based controls and risk acceptance criteria, triggering corrective actions automatically. Core financial workflows — accounting, treasury, risk reporting, and regulatory compliance controls — are rich with event-driven patterns that demand immediate attention. This paper proposes an architecture that brings the financial infrastructure in line with the rest of the enterprise, making these real-time capabilities readily accessible within commercial financial systems.
Intelligent Event-Driven Platforms (IEPs) provide the foundation for such environments. Here, "intelligent" describes not the platform itself but the data-driven decisioning enabled by the Event-Data-Response Model™, with real-time processing as a core design requirement. The paper first examines event-driven architecture to establish its fit for enterprise-scale demands and to identify the distinctive characteristics of publish-subscribe patterns in this context. It then details enterprise-financial business processes, addressing the event types involved, the relevant objects of concern, data-source synchronization, and the roles of owners and approving authorities in supporting compliance. Finally, the IEP framework is extended to specify requirements for unified enterprise event streams and topics, along with event-schema management.
References
1. Adams, N., Augusto, A., Davern, M. J., & La Rosa, M. (2022). On the role of process mining in business process compliance. SSRN Electronic Journal.
2. Agostinelli, S., Chiariello, F., Maggi, F. M., Marrella, A., & Patrizi, F. (2023). Process mining meets model learning: Discovering deterministic finite state automata from event logs for business process analysis. Information Systems, 114, 102180.
3. Alzhrani, F., Saeedi, K., & Zhao, L. (2024). A process-aware framework to support process mining from blockchain applications. Journal of King Saud University Computer and Information Sciences, 36, 101956.
4. Venkateswara Rao Movva. (2024). AI-Augmented DevOps for Large-Scale Retail Platforms: A Framework for Intelligent CI/CD, Automated Testing, and Deployment Optimization. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 7759–7765. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5827
5. Augusto, A., Dumas, M., La Rosa, M., Leemans, S. J. J., & vanden Broucke, S. K. L. M. (2021). Optimization framework for DFG-based automated process discovery approaches. Software and Systems Modeling, 20, 1245–1270.
6. Badakhshan, P., Wurm, B., Grisold, T., Geyer-Klingeberg, J., Mendling, J., & vom Brocke, J. (2022). Creating business value with process mining. The Journal of Strategic Information Systems, 31(4), 101745.
7. Bayomie, D., Di Ciccio, C., & Mendling, J. (2023). Event-case correlation for process mining using probabilistic optimization. Information Systems, 114, 102167.
8. Pranitha Potturi. (2024). AI-Augmented Analytics Architecture for Intelligent Enterprise Reporting and Predictive Business Decision-Making . Journal of Computational Analysis and Applications (JoCAAA), 33(05), 3830–3841. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5797
9. Camargo, M., Báron, D., Dumas, M., & González-Rojas, O. (2023). Learning business process simulation models: A hybrid process mining and deep learning approach. Information Systems, 117, 102248.
10. Chapela-Campa, D., & Dumas, M. (2024). From process mining to augmented process execution. Software and Systems Modeling, 22, 1977–1986.
11. Elkoumy, G., Pankova, A., & Dumas, M. (2023). Differentially private release of event logs for process mining. Information Systems, 115, 102161.
12. Loganathan, R., & Kolla, S. H. (2024). Harnessing generative AI for adaptive risk policy generation in multi-regulatory data center environments. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(3), 505-515.
13. González, L., Delgado, A., Canaparo, J., & Gambetta, F. (2022). Evaluation of compliance requirements for collaborative business process with process mining and a model of generic compliance controls. CLEI Electronic Journal, 25(2).
14. Imran, M., Ismail, M. A., Hamid, S., & Md Nasir, M. H. N. (2022). Complex process modeling in process mining: A systematic review. IEEE Access, 10, 101515–101536.
15. Jans, M., De Weerdt, J., Depaire, B., Dumas, M., & Janssenswillen, G. (2021). Conformance checking in process mining. Information Systems, 102, 101851.
16. Sasidhar Reddy Mondeddula. (2024). Large Language Model-Based Financial Intelligence for Automated Compliance, Risk Analysis, and Regulatory Reporting. Journal of Computational Analysis and Applications (JoCAAA), 33(06), 4222–4227. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5813
17. Khriji, S., Benbelgacem, Y., Chéour, R., et al. (2022). Design and implementation of a cloud-based event-driven architecture for real-time data processing in wireless sensor networks. The Journal of Supercomputing, 78, 3374–3401.
18. Lashkevich, K., Milani, F., Chapela-Campa, D., Suvorau, I., & Dumas, M. (2024). Unveiling the causes of waiting time in business processes from event logs. Information Systems, 126, 102434.
19. Leemans, S. J. J., Fahland, D., & van der Aalst, W. M. P. (2021). Discovering block-structured process models from incomplete event logs. Information Systems, 101, 101522.
20. Leno, V., Polyvyanyy, A., Dumas, M., La Rosa, M., & Maggi, F. M. (2021). Robotic process mining: Vision and challenges. Business & Information Systems Engineering, 63, 301–314.
21. Mattaparthi, R. (2024). Transformer-Based Fault Diagnosis for Large-Scale Standby Power Generators: Partial Discharge Pattern Recognition at Hyperscale Data Center Installations. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 8781-8799.
22. Mamudu, A., Bandara, W., Leemans, S. J. J., & Wynn, M. T. (2023). A process mining impacts framework. Business Process Management Journal, 29(3), 690–709.
23. Martin, N., Fischer, D. A., Kerpedzhiev, G. D., Goel, K., Leemans, S. J. J., Röglinger, M., van der Aalst, W. M. P., Dumas, M., La Rosa, M., & Wynn, M. T. (2021). Opportunities and challenges for process mining in organizations: Results of a Delphi study. Business & Information Systems Engineering, 63, 511–527.
24. Monti, F., Mathew, J. G., Leotta, F., & Koschmider, A. (2024). On the application of process management and process mining to Industry 4.0. Software and Systems Modeling, 23, 1407–1419.
25. Park, G., & van der Aalst, W. M. P. (2022). Action-oriented process mining: Bridging the gap between insights and actions. Progress in Artificial Intelligence, 11, 333–352.
26. Kolla, S. H., & Peddi, R. K. (2024). Designing Governance-Aligned GenAI Pipelines Using Small Language Models for Enterprise Workflow Intelligence. International Journal of Science, Research and Technology, 7(6), 13256-13268.
27. Rinderle-Ma, S., Winter, K., & Benzin, J.-V. (2023). Predictive compliance monitoring in process-aware information systems: State of the art, functionalities, research directions. Information Systems, 121, 102210.
28. Saini, A. K., Kamra, R., & Shrivastava, U. (2021). Conformance checking techniques of process mining: A survey. In Recent Trends in Intensive Computing. IOS Press.
29. Penaka, B. (2024). Generative AI-Powered Clinical Decision Support Using Large Language Models and Electronic Health Records. Journal of Neonatal Surgery, 13(1), 2588–2596. Retrieved from https://jneonatalsurg.com/index.php/jns/article/view/10577
30. Singh, S. K., & Jenamani, M. (2024). ProcessChain: A blockchain-based framework for privacy preserving cross-organizational business process mining from distributed event logs. Business Process Management Journal, 30(1), 239–269.
31. Taymouri, F., La Rosa, M., Dumas, M., & Maggi, F. M. (2021). Business process variant analysis: Survey and classification. Knowledge-Based Systems, 211, 106557.
32. van der Aalst, W. M. P. (2021). Process mining: A 360 degree view. In Proceedings of the International Conference on Process Mining.
33. vom Brocke, J., Jans, M., Mendling, J., Reijers, H. A., et al. (2021). A five-level framework for research on process mining. Business & Information Systems Engineering, 63, 483–490.
34. Rajesh Reddy Ellenki. (2024). Deep Learning-Based Demand Intelligence for Dynamic Merchandise Planning and Allocation. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 7796–7802. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5832
35. Ali, M. A., Milani, F., & Dumas, M. (2024). Data-driven identification and analysis of waiting times in business processes. Business & Information Systems Engineering.
36. Burke, A. T., Leemans, S. J. J., Wynn, M. T., van der Aalst, W. M. P., & ter Hofstede, A. H. M. (2024). A chance for models to show their quality: Stochastic process model-log dimensions. Information Systems, 125, 102382.
37. Goel, K., Martin, N., & ter Hofstede, A. H. M. (2024). Demystifying data governance for process mining: Insights from a Delphi study. Information & Management, 61, 103973.
38. López-Pintado, O., Dumas, M., & Berx, J. (2024). Discovery, simulation, and optimization of business processes with differentiated resources. Information Systems, 120, 102289.
39. Ali, M. A., Milani, F., & Dumas, M. (2024). Data-driven identification and analysis of waiting times in business processes. Business & Information Systems Engineering.
40. Caballero Villalobos, J. (2024). A hybrid approach to compliance checking: Law meets process domain. In Proceedings of the 6th International Conference on Process Mining.
41. Reddy, V. A. R. (2024). Generative Intelligence for Healthcare Claims Processing and Personalized Benefits Management. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 7(3), 14099.
42. Mamudu, A., Bandara, W., Wynn, M. T., & Leemans, S. J. J. (2024). Process mining success factors and their interrelationships. Business & Information Systems Engineering, 67, 389–408.
43. Singh, S. K., & Jenamani, M. (2024). ProcessChain: A blockchain-based framework for privacy preserving cross-organizational business process mining from distributed event logs. Business Process Management Journal, 30(1), 239–269.
44. Lashkevich, K., Milani, F., Chapela-Campa, D., Suvorau, I., & Dumas, M. (2024). Unveiling the causes of waiting time in business processes from event logs. Information Systems, 126, 102434.
Additional Files
Published
Data Availability Statement
None
Issue
Section
License
Copyright (c) 2025 Bhasker Katta (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons
Attribution 4.0 International License (CC BY 4.0).
You are free to:
- Share: copy and redistribute the material
- Adapt: remix, transform, and build upon the material
for any purpose, even commercially.
Under the following terms:
Attribution — You must give appropriate credit to
the original author(s) and source.
Full license text: https://creativecommons.org/licenses/by/4.0/
