Preventing the Financing of Terrorism with Machine Learning and Blockchain Data

In this high-impact two-month Omdena Challenge, a global team of 50 AI changemakers worked in close collaboration with the UN Office of Counter Terrorism and the goFintel project, to engineer a model to correlate data from financial, criminal, and other records to identify and prevent and counter the financing of terrorism.

This project runs on an open-source license.

The Problem & Background 

goFintel is the “NEXT Generation platform for Detecting, Preventing and Countering Money Laundering and the Financing of Terrorism” 

Under the framework of the UN Office of Counter Terrorism “UN-OICT-UNCCT Global coordinated programme on detecting, preventing and countering the financing of terrorism” addressing UN Security Council Resolution 2462 and 2482 mandates, the UN initiated the goFintel project with the aim to strengthen Member States capacity to combat money laundering and the financing of terrorism.

goFintel seeks to shift away from a siloed approach to intelligence and expand money laundering and terrorism financing investigation capabilities through analysis that spans multiple and different data sources. This approach enables Financial Intelligence Units (FIU) to rely on advanced information sharing, data transmission and retention, secure ICT architectures, and cross-functional operational capabilities with multiple stakeholders (e.g., law enforcement, judiciary, etc.).

goFintel

The project outcomes

Omdena team of 50 AI changemakers worked in close collaboration with the goFintel project, to undertake the challenge of engineering a model to correlate data from financial, criminal, and other records to identify and prevent and counter the financing of terrorism.

The deliverables of this project are as follows: 

• Correlate data from various sectors using supervised and unsupervised learning system
• Display / Visualize data in many formats based on names, times, locations, travel records, sums of money, etc.
• Visualize on-demand and in real-time.

The Data

Financial Crime Data:

• Suspicious Transaction Reports / Cash Transaction Reports (STRs/CTRs), test data sample will be provided 
• Open Source financial crime records / Company registers etc. available on the public web
• United Nations Security Council Consolidated List
• Other data sets publicly available (social media, OpenSource intelligence, etc).