Applying Graph Theory to Detect Cases of Money Laundering and Terrorism Financing

Abstract

A technique to automate the generation of criminal cases for money laundering and financing of terrorism (ML/FT) based on typologies is proposed. That will help an automated system from making a decision about the exact coincidence when comparing the case objects and their links with those in the typologies. Several types of subgraph changes (mutations) are examined. The main goal to apply these mutations is to consider other possible ML/FT variants that do not correspond explicitly to the typologies but have a similar scenario. Visualization methods like the graph theory are used to order perception of data and to reduce its volumes. This work also uses the foundations of information and financial security. The research demonstrates possibilities of applying the graph theory and big data tools in investigating information security incidents. A program has been written to verify the technique proposed. It was tested on case graphs built on the typologies under consideration.