Leveraging Artificial Intelligence for Trade-Based Money Laundering Detection: A Machine Learning Approach for Anomaly Detection in Letters of Credit and Bank Guarantees

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.