Authors : Pankaj kumar Tejraj Jain; Ashok Ghimire

Volume/Issue : Volume 10 - 2025, Issue 3 - March

Google Scholar : https://tinyurl.com/yu67v6xj

Scribd : https://tinyurl.com/5e3xjruw

DOI : https://doi.org/10.38124/ijisrt/25mar1925

Google Scholar

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Note : Google Scholar may take 15 to 20 days to display the article.

Abstract : The world of international trade and finance is becoming increasingly complex, which has turned trade-based money laundering (TBML) into a major headache for banks and regulatory agencies. Traditional ways of spotting suspicious activities in trade finance—like manual checks and rule-based systems—often struggle to keep up with the ever-changing tactics used by money launderers. This paper dives into how artificial intelligence (AI) and machine learning (ML) can be leveraged to improve the detection of TBML in trade finance, with a particular focus on Letters of Credit (LCs) and Bank Guarantees (BGs). By using machine learning models for spotting anomalies, we suggest a method that can automatically sift through trade documents, transaction patterns, and the parties involved in trade financing to pinpoint irregularities. Our model utilizes both supervised and unsupervised learning algorithms to reveal hidden connections between entities and transactions, making it easier to identify potential TBML cases with greater precision and efficiency. The study underscores the need to integrate advanced AI techniques, like natural language processing (NLP) and anomaly detection, to create scalable solutions that bolster the effectiveness of anti-money laundering (AML) efforts in trade finance. Early test results show that this approach could significantly cut down on false positives, enhance detection rates, and ultimately aid in preventing financial crimes linked to international trade.

Keywords : Artificial Intelligence (AI), Machine Learning (ML), Trade-Based Money Laundering (TBML), Anomaly Detection, Letters of Credit (LCs), Bank Guarantees (BGs), Anti-Money Laundering (AML), Natural Language Processing (NLP), Transaction Patterns, Suspicious Activity Detection, Financial Crime Prevention, Supervised Learning, Unsupervised Learning.

References :

1. Elahi Nezhad, M., Rashidian, S., & Botta, C. (2024). Revolutionizing trade finance: leveraging the power of blockchain and AI in electronic letters of credit. Uniform Law Review, 29(1), 87-115.
2. Fakih, M. (2022). Trade-Based Money Laundering (Doctoral dissertation, Lebanese American University).
3. Khan, H. U., Malik, M. Z., & Nazir, S. (2024). Identifying the AI-based solutions proposed for restricting Money Laundering in Financial Sectors: Systematic Mapping. Applied Artificial Intelligence, 38(1), 2344415.
4. Lupton, C., & Reddy, S. (2025). Combating financial crime: the potential and regulation of artificial intelligence. Sydney Law Review, 43, 44.
5. OLORUNTOBA, O. (2024). Generative AI for Creative Data Management: Optimizing Database Systems in the Creative Industry.
6. Mallik, S. K., Islam, M. R., Uddin, I., Ali, M. A., & Trisha, S. M. (2025). Leveraging artificial intelligence to mitigate money laundering risks through the detection of cyberbullying patterns in financial transactions. Global Journal of Engineering and Technology Advances, 22(01), 094-115.
7. Cassara, J. A. (2015). Trade-based money laundering: the next frontier in international money laundering enforcement. John Wiley & Sons.
8. Begum, A., Munira, M. S. K., & Juthi, S. (2022). SYSTEMATIC REVIEW OF BLOCKCHAIN TECHNOLOGY IN TRADE FINANCE AND BANKING SECURITY. American Journal of Scholarly Research and Innovation, 1(01), 25-52.
9. Oloruntoba, N. O. (2025). AI-Driven autonomous database management: Self-tuning, predictive query optimization, and intelligent indexing in enterprise it environments. World Journal of Advanced Research and Reviews, 25(2), 1558-1580.
10. van de Pol, D. G. Laundromats: More Than Just Missing Socks.
11. Frunza, M. C. (2015). Solving modern crime in financial markets: Analytics and case studies. Academic Press.
12. Ιωσηφίδου, Ε. Μ. (2023). Financial fraud detection.
13. Gowhor, H. S. (2021). Identification of effectiveness criteria for financial intelligence tools in early detection of terrorist financing activities (Doctoral dissertation, Macquarie University).
14. Remeikienė, R., & Gaspareniene, L. (2023). Effects on the soundness of financial-banking institutions and on the business development. In Economic and Financial Crime, Sustainability and Good Governance (pp. 235-269). Cham: Springer International Publishing.
15. Vulli, M. (2022). EFFECTIVENESS OF THE AML REGIME.
16. Sekgoka, C. P. (2021). Modeling Cross-Border Financial Flows Using a Network Theoretic Approach (Doctoral dissertation, University of Pretoria (South Africa)).
17. Böffel, L., & Schürger, J. (Eds.). (2022). Digitalisation, Sustainability, and the Banking and Capital Markets Union: Thoughts on Current Issues of EU Financial Regulation. Springer Nature.
18. Oloruntoba, O. Architecting Resilient Multi-Cloud Database Systems: Distributed Ledger Technology, Fault Tolerance, and Cross-Platform Synchronization.
19. Bauskar, S. (2025). Leveraging AI for Intelligent Data Management in Multi-Cloud Database Architectures. International Journal of Sustainable Development in computer Science Engineering, 11(11), 1-12.