TRM: Solving AML Problems Via Collective Intelligence

As money laundering techniques are growing in volume and complexity, banks can no longer sustain fighting financial crimes the conventional way by just focussing on rules-based, siloed detection with no or limited insights from peer banks. This paper begins with analysing the current state of the AML Transaction Monitoring (TM) ecosystem, highlighting the challenges across legacy systems and traditional machine learning applications. In the next segment, the paper highlights Tookitaki’s innovation in TM through Typology Repository Management (TRM), a new way of detecting money laundering through collective intelligence and continuous learning. At Tookitaki, we envision that this advanced machine learning approach will enable financial institutions to capture changing customer behaviour and stop the bad actors with high accuracy and speed, improving returns and risk coverage.

Tookitaki TRM concept was awarded the Monetary Authority of Singapore’s Financial Sector Technology and Innovation (FSTI) Proof of Concept (POC) grant in December 2019. The FSTI POC grant provides funding support for experimentation, development and dissemination of nascent innovative technologies in the financial services sector.

Download the whitepaper below.