Cost-Aware Event Screening for Financial Compliance
Keywords:
Event Stream Compliance Intelligence Framework; Financial Screening; Risk Governance; Cost Awareness; Real Time Decision Making; Event Stream Processing; Regulatory Intelligence; Compliance Intelligence; Baseline Compliance Monitoring; Event Stream Processing.Abstract
The Event-Stream Compliance Intelligence Framework presents a conceptual model for data-driven risk governance, with crime prevention and cost control as its central aims. It brings intelligence, monitoring, and screening together in a single system. By applying event-stream processing to internal and external data feeds in near real time, the framework supports timely event detection and risk governance. It assigns clear roles: who is accountable for compliance, who assesses newly identified violations, and at what point those assessments enter cost-aware risk management. Data normalization performed ahead of analysis ensures that the objects reported on are those most valuable for detecting crime and reducing losses. Together, these elements allow screening functions to be expressed in terms of minimizing risk-adjusted expected losses. Success is judged by detection accuracy, which is tied directly to total cost of ownership.
Financial screening for crime, fraud, and litigation is essential to protecting the organization's reputation and shareholder assets. Confirming that those who transact business on the organization's behalf comply with key regulations, laws, and best practices is the foundation for preventive crime controls. Monitoring intelligence gathers and refines information from diverse sources to detect a broad range of potential violations of restrictions, regulatory requirements, and business constraints. A further pillar of risk governance is engaging the appropriate governance team to assess suspected new crimes, frauds, or policy infractions. This step can be central to stopping some crimes before they occur and, at a minimum, to reducing their likelihood, severity, or financial impact.
References
1. Baesens, B., Höppner, S., & Verdonck, T. (2021). Data engineering for fraud detection. Decision Support Systems, 150, 113492.
2. Błaszczyński, J., de Almeida Filho, A. T., Matuszyk, A., Szeląg, M., & Słowiński, R. (2021). Auto loan fraud detection using dominance-based rough set approach versus machine learning methods. Expert Systems with Applications, 163, 113740.
3. Canhoto, A. I. (2021). Leveraging machine learning in the global fight against money laundering and terrorism financing: An affordances perspective. Journal of Business Research, 131, 441–452.
4. Srinivas Kalisetty. (2023). Big Data–Driven Cloud Collaboration Models for Enhancing Supplier–Retailer Synchronization in Modern Manufacturing Supply Chains. Journal of Computational Analysis and Applications (JoCAAA), 31(4), 2188–2205. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/4232
5. Cheng, C.-H., Kao, Y.-F., & Lin, H.-P. (2021). A financial statement fraud model based on synthesized attribute selection and a dataset with missing values and imbalanced classes. Applied Soft Computing, 108, 107487.
6. Domashova, J., & Mikhailina, N. (2021). Usage of machine learning methods for early detection of money laundering schemes. Procedia Computer Science, 190, 184–192.
7. Pamisetty, V. (2022). Making IoT Data Processing Scalable: Architectures That Actually Work. International Journal of Scientific Research and Modern Technology, 281. https://doi.org/10.38124/ijsrmt.v1i12.1187
8. Gupta, A., Dwivedi, D. N., Shah, J., & Jain, A. (2021). Data quality issues leading to sub optimal machine learning for money laundering models. Journal of Money Laundering Control, 25(3), 551–555.
9. Izotova, A., & Valiullin, A. (2021). Comparison of Poisson process and machine learning algorithms approach for credit card fraud detection. Procedia Computer Science, 186, 721–726.
10. Shou, M., Bao, X., & Yu, J. (2021). An optimal weighted machine learning model for detecting financial fraud. Applied Economics Letters, 30(4), 410–415.
11. Stojanović, B., Božić, J., Hofer-Schmitz, K., Nahrgang, K., Weber, A., Badii, A., Sundaram, M., Jordan, E., & Runevic, J. (2021). Follow the trail: Machine learning for fraud detection in FinTech applications. Sensors, 21(5), 1594.
12. Danda, R. R., Maguluri, K. K., Yasmeen, Z., Mandala, G., & Dileep, V. (2023). Intelligent Healthcare Systems: Harnessing Ai and Ml To Revolutionize Patient Care And Clinical Decision-Making. International Journal of Applied Engineering and Technology https://papers. ssrn. com/sol3/papers. cfm.
13. Hilal, W., Gadsden, S. A., & Yawney, J. (2022). Financial fraud: A review of anomaly detection techniques and recent advances. Expert Systems with Applications, 193, 116429.
14. Hayble-Gomes, E. (2022). The use of predictive modeling to identify relevant features for suspicious activity reporting. Journal of Money Laundering Control, 26(4), 806–830.
15. Jensen, R. I. T., & Iosifidis, A. (2022). Fighting money laundering with statistics and machine learning. IEEE Access, 11, 43887–43902.
16. Jensen, R. I. T., & Iosifidis, A. (2023). Qualifying and raising anti-money laundering alarms with deep learning. Expert Systems with Applications, 214, 119037.
17. Meda, R., & Pamisetty, A. (2023). Intelligent Infrastructure for Real-Time Inventory and Logistics in Retail Supply Chains. Educational Administration: Theory and Practice, 29 (4), 5215–5233.
18. Lokanan, M. E. (2022). Predicting money laundering using machine learning and artificial neural networks algorithms in banks. Journal of Applied Security Research, 19(1), 20–44.
19. Ouazzane, K., Polykarpou, T., Patel, Y., & Li, J. (2022). An integrated machine learning framework for fraud detection: A comparative and comprehensive approach. International Journal of Information Security and Privacy, 16(1), 1–17.
20. Vorobyev, I., & Krivitskaya, A. (2022). Reducing false positives in bank anti-fraud systems based on rule induction in distributed tree-based models. Computers & Security, 120, 102786.
21. Achakzai, M. A. K., & Juan, P. (2022). Using machine learning Meta-Classifiers to detect financial frauds. Finance Research Letters, 48, 102915.
22. Khan, A. T., Cao, X., Li, S., Katsikis, V. N., Brajevic, I., & Stanimirovic, P. S. (2022). Fraud detection in publicly traded U.S. firms using Beetle Antennae Search: A machine learning approach. Expert Systems with Applications, 191, 116148.
23. Pandugula, C., & Nampalli, R. C. R. Optimizing Retail Performance: Cloud-Enabled Big Data Strategies for Enhanced Consumer Insights.
24. Lin, K., & Gao, Y. (2022). Model interpretability of financial fraud detection by group SHAP. Expert Systems with Applications, 210, 118354.
25. Tuffveson Jensen, R. I., & Iosifidis, A. (2022). Time-aware and interpretable predictive monitoring system for anti-money laundering. Machine Learning with Applications, 8, 100306.
26. Naveed, N., Munawar, S., & Usman, A. (2023). Intelligent anti-money laundering fraud control using graph-based machine learning model for the financial domain. Journal of Cases on Information Technology, 25(1).
27. Alexandre, C. R., & Balsa, J. (2023). Incorporating machine learning and a risk-based strategy in an anti-money laundering multiagent system. Expert Systems with Applications, 217, 119500.
28. Pavlidis, G. (2023). Deploying artificial intelligence for anti-money laundering and asset recovery: The dawn of a new era. Journal of Money Laundering Control, 26(7), 155–166.
29. Yandamuri, U. S. (2023). An Intelligent Analytics Framework Combining Big Data and Machine Learning for Business Forecasting. Zenodo.
30. Tong, G., & Shen, J. (2023). Financial transaction fraud detector based on imbalance learning and graph neural network. Applied Soft Computing, 149, 110984.
31. Yi, Z., Cao, X., Pu, X., Wu, Y., Chen, Z., Khan, A. T., Francis, A., & Li, S. (2023). Fraud detection in capital markets: A novel machine learning approach. Expert Systems with Applications, 231, 120760.
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