© Marco Tjokro

Detecting money laundering: An intelligent system for automatic detection of money laundering typologies from FINTRAC disclosures

Grant Agency: Office of Crime Reduction and Gang Outreach (OCR-GO)’s Crime Reduction Research Program

Awarded: 2020-2021

Principal Investigator: Dr. Richard Frank

Co-Principal Investigator: Ashleigh Gonzales

Research Assistants: Ruby Ling, Myfanwy Thomson

This research study aims to conduct a comprehensive analysis of money laundering typologies and crime scripts from anti-money laundering (AML) literature and to develop a data-driven artificial intelligence solution to automatically detect money laundering typologies from Financial Transactions and Reports Analysis Centre of Canada (FINTRAC) disclosure data. A money laundering typology refers to the methods, techniques, and trends in money laundering. Features of a money laundering typology may include specific schemes or techniques, patterns, professions, or instruments. Vital objectives of the current study are to conduct a review and analysis of the current state of information sharing in support of a proof of concept computational model that improves intelligence processes, while also providing recommendations that derive from the study on future paths forward for AML intelligence cycle and process design. This project is conducted in partnership with the Counter Illicit Finance Alliance BC (CIFA-BC, previously known as Project ATHENA) and the Crime Reduction Research Program, and administered in partnership by the Ministry of Public Safety and Solicitor General, the Combined Forces Special Enforcement Unit of British Columbia, and the RCMP "E" Division.