Event-Driven Sanctions Screening
Keywords:
Adaptive Sanctions Screening, Sanctions Compliance, Financial Crime Compliance, False Positive Reduction, Risk Scoring, Privacy-Enhancing Technologies (PETs), Data Protection Impact Assessment (DPIA), Cross-Border Data Transfers, Regulatory Compliance, Financial Intelligence.Abstract
A growing number of countries and regions are imposing sanctions on entities and individuals in third-party jurisdictions, leading to increasing divergence between sanctioning authorities—particularly within financial services and trade. As a result, banks and companies frequently encounter customers, business partners, or transactions flagged as potential sanctions matches, many of which are ultimately cleared after further investigation. Even when sanctions are properly respected, these alerts carry a cost in terms of investigative time, and the risk of reputational damage grows with the volume of alerts raised. Adaptive sanctions screening systems aim to address this challenge by reducing the number of false positives. However, designing such systems is far from straightforward: the diversity of sanctions regimes, and the signals that may indicate their violation, can themselves be leveraged as opportunities for improvement. Risk scores can help prioritise sanctions alerts for further review, while external sources of information can provide early indicators of deteriorating consumer or business reputation.
References
1. Fadavi, A. (2023). Economic sanctions on the rise: The ever-increasing importance of sanctions screening in a compliance programme. Journal of Financial Compliance, 6(4), 333–345.
2. Mashetty, S. (2024). Leveraging Deep Learning, Neural Networks, and Data Engineering for Intelligent Mortgage Loan Validation: A Data-Driven Approach to Automating Borrower Income, Employment, and Asset Verification. Neural Networks, and Data Engineering for Intelligent Mortgage Loan Validation: A Data-Driven Approach to Automating Borrower Income, Employment, and Asset Verification (December 21, 2024).
3. Kim, S., & Yang, S. (2024). Accuracy improvement in financial sanction screening: Is natural language processing the solution? Frontiers in Artificial Intelligence, 7, Article 1374323.
4. Turksen, U., Benson, V., & Adamyk, B. (2024). Legal implications of automated suspicious transaction monitoring: Enhancing integrity of AI. Journal of Banking Regulation, 25(4), 359–377.
5. Movva, V. R. (2024). Cloud-Native Platform Engineering for High-Scale Retail Systems: Designing Resilient Microservices and Intelligent Infrastructure. Available at SSRN 7449480.
6. Redhead, M. (2021). The future of transaction monitoring: Better ways to detect and disrupt financial crime. SWIFT Institute Working Paper.
7. Financial Action Task Force. (2021). Guidance on proliferation financing risk assessment and mitigation.
8. Mahadevan, S. (2024). Empowering Manufacturing: Generative AI Revolutionizes ERP Application. Int. J. Innov. Sci. Res, 9, 593-595.
9. Financial Action Task Force. (2021). Updated guidance for a risk-based approach to virtual assets and virtual asset service providers.
10. Manukonda, K. R. R. (2024). Chapter-5 Enhancing Test Automation Efficiency Through AI-driven Reporting: An Empirical Study on Defect Detection and Resolution Time. ADVANCES IN, 119, 107.
11. Financial Action Task Force. (2022). Targeted update on implementation of the FATF standards on virtual assets and virtual asset service providers.
12. Financial Action Task Force. (2023). Targeted update on implementation of the FATF standards on virtual assets and virtual asset service providers.
13. Financial Action Task Force. (2024). Anti-money laundering and counter-terrorist financing measures in India: Mutual evaluation report.
14. Mahadevan, S., Desani, N. R., Yadav, P. S., Joseph, T., & Malcom, A. (2024). FutureTech Innovations. Cari Journals USA LLC.
15. Office of Foreign Assets Control. (2021). Sanctions compliance guidance for the virtual currency industry. U.S. Department of the Treasury.
16. Office of Foreign Assets Control. (2022). Sanctions compliance guidance for the virtual currency industry. U.S. Department of the Treasury.
17. Office of Foreign Assets Control. (2023). Sanctions compliance guidance for the virtual currency industry. U.S. Department of the Treasury.
18. KOLLA, S. K. (2024). DEEP LEARNING AND CLINICAL NLP FRAMEWORK FOR AUTOMATED KNOWLEDGE EXTRACTION FROM MULTIMODAL HEALTH . TPM – Testing, Psychometrics, Methodology in Applied Psychology, 31(S2), 134–140. Retrieved from https://tpmap.org/submission/index.php/tpm/article/view/4748
19. Office of Foreign Assets Control. (2024). Sanctions compliance guidance for the maritime shipping industry. U.S. Department of the Treasury.
20. Alkhalili, M., Qutqut, M. H., & Almasalha, F. (2021). Investigation of applying machine learning for watch-list filtering in anti-money laundering. IEEE Access, 9, 18481–18496.
21. Charoenwong, B., & Reddy, P. (2022). Using forensic analytics and machine learning to detect bribe payments in regime-switching environments: Evidence from the India demonetization. PLOS ONE, 17(6), Article e0268965.
22. Jensen, R. I. T., & Iosifidis, A. (2023). Qualifying and raising anti-money laundering alarms with deep learning. Expert Systems with Applications, 214, Article 119037.
23. 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, Article 119500.
24. Tuffveson, R. I., & Iosifidis, A. (2022). Time-aware and interpretable predictive monitoring system for anti-money laundering. Machine Learning with Applications, 8, Article 100306.
25. van Liebergen, B. (2023). Fighting money laundering with statistics and machine learning. IEEE Access, 11, 8889–8903.
26. Pocher, N., Zichichi, M., Merizzi, F., Shafiq, M. Z., et al. (2023). Detecting anomalous cryptocurrency transactions: An AML/CFT application of machine learning-based forensics. Electronic Markets, 33, Article 37.
27. Lokanan, M. E. (2023). Predicting mobile money transaction fraud using machine learning algorithms. Applied AI Letters, 4(2), Article e85.
28. Gaddam, S. R. (2022). Automating regulatory compliance screening in financial services: Engineering high-throughput sanctions data pipelines at enterprise scale. International Journal of Communication Networks and Information Security, 14(3), 1611–1624.
29. Financial Action Task Force. (2023). Guidance on beneficial ownership of legal persons.
30. Manukonda, K. R. R., Swamy, H., Sankar, R., & CM, C. (2024, August). Application of Explicit Group Sparse Projection in Optimizing 5G Network Resource Allocation and Topology Design. In 2024 4th Asian Conference on Innovation in Technology (ASIANCON) (pp. 1-6). IEEE.
31. Financial Action Task Force. (2024). Guidance on beneficial ownership and transparency of legal arrangements.
32. Financial Action Task Force. (2021). Cross-border payments: Survey results on implementation of the FATF standards.
33. Financial Action Task Force. (2022). China: Follow-up report.
34. Financial Action Task Force. (2023). Jurisdictions under increased monitoring: February 2023.
35. Financial Action Task Force. (2024). United States: Seventh follow-up report.
36. Financial Action Task Force. (2024). Kenya: Follow-up report.
37. Financial Action Task Force. (2021). Japan: Mutual evaluation report.
38. European Banking Authority. (2021). Report on the use of innovative solutions by credit institutions and financial institutions in the COVID-19 context.
39. European Banking Authority. (2022). Opinion on the risks of money laundering and terrorist financing affecting the European Union's financial sector.
40. European Banking Authority. (2023). Report on the use of machine learning and artificial intelligence in the EU banking sector.
41. Office of Foreign Assets Control. (2024). Advisory and guidance on potential sanctions risks arising from dealings in high-value artwork. U.S. Department of the Treasury.
42. Office of Foreign Assets Control. (2024). Advisory for the maritime oil industry and related sectors. U.S. Department of the Treasury.
