A Risk Scoring Model for Managing Money Laundering Transactions
Mar 26, 2025
The paper released by the Research and Innovation Centre of Rabdan Academy in collaboration with MENA FCCG’s Working Group on AI and ADGM Academy Research Centre introduces a machine learning alert scoring model to improve risk classifications for AML alerts. Money laundering detection remains a global challenge, with high volumes of false positives overwhelming compliance teams. The report can serve as a blueprint for financial institutions looking to modernise their money laundering detection practices with AI.
The paper presents a high-level overview of the results of implementing a machine learning model for evaluating a risk score for anti-money laundering alerts and the impact on operational efficiency. It outlines how this was achieved by enhancing the existing rules-based transaction monitoring process with a complementary machine learning model. The model would allow compliance professionals to better manage risk by auto-escalating high-risk alerts and hibernating low-risk alerts.
Rabdan Academy is a government-owned world-class education institution established to coordinate and enhance learning outcomes for organisations and individuals in the Safety, Security, Defence, Emergency Preparedness and Crisis Management (SSDEC) Sectors.
ADGM Academy is part of Abu Dhabi Global Market (ADGM), an International Financial Centre (IFC) located in the UAE.
Authors:
Dr Eric Halford - Rabdan Academy
Dr Ian Gibson – Rabdan Academy
Members of MENA FCCG's Working Group on AI
Download the report below:
