TRACE Reasoning Mechanism Approach to Illicit Money Flows Detection

The paper reports on the TRACE AI-enabled tracing of illicit money flows for law enforcement, with these specific objectives to (1) create a reasoning mechanism, (2) develop intelligence analytics and visualisation, and (3) create knowledge graph, system architecture and case management. The solution highlights significant progress in stylometric analysis underpinned by machine learning (MLR) and deep learning (DL) methodologies. This advancement shapes a more robust decision-making framework for the TRACE reasoning mechanism, improving the accuracy and reliability of fraud detection. This mechanism systematically processes diverse data sources, uncovers hidden money laundering patterns, and facilitates informed investigation decision-making, significantly aiding LEAs.
The TRACE solution demonstrates how integrating state-of-the-art natural language processing (NLP) tools with MLR techniques improves the detection of concealed patterns indicative of money laundering. Combining human crowd intelligence with advanced DL techniques has proven effective in processing high-volume queries and extracting relevant information from large data sets. AI tools complement human efforts by assisting in data aggregation, analysis, and pattern recognition. The TRACE reasoning mechanism identifies common patterns in money laundering to improve decision-making and offer information that may be used as evidence in legal cases.