Using dynamic risk estimation & social network analysis to detect money laundering evolution

Abstract

We propose a risk model for money laundering that assigns a risk value for transactions being a part of a larger chain of transactions that may be a part of a money laundering scheme. We use social networks to connect missing links in potential transaction sequences. Taken together we can provide a financial sector independent risk assessment to submitted transactions. The proposed risk model is validated using data from realistic scenarios and our already developed money laundering evolution detection framework (MLEDF). MLEDF uses sequence matching, case-based analysis, social network analysis, and complex event processing to link fraudulent transaction trails - a series of linked money laundering schemes. MLEDF has components to collect data, run them against business rules and evolution models, run detection algorithms and use social network analysis to connect potential participants.