Prior Publisher

The Association of Digital Forensics, Security and Law (ADFSL)


Money launderers hide traces of their transactions with the involvement of entities that participate in sophisticated schemes. Money laundering detection requires unraveling concealed connections among multiple but seemingly unrelated human money laundering networks, ties among actors of those schemes, and amounts of funds transferred among those entities. The link among small networks, either financial or social, is the primary factor that facilitates money laundering. Hence, the analysis of relations among money laundering networks is required to present the full structure of complex schemes. We propose a framework that uses sequence matching, case-based analysis, social network analysis, and complex event processing to detect money laundering. Our framework captures an ongoing single scheme as an event, and associations among such ongoing sequence of events to capture complex relationships among evolving money laundering schemes. The framework can detect associated multiple money laundering networks even in the absence of some evidence. We validated the accuracy of detecting evolving money laundering schemes using a multi-phases test methodology. Our test used data generated from real-life cases, and extrapolated to generate more data from real-life schemes generator that we implemented.


Gunestas, M., Wijesekera, D., & Singhal, A. (2008). Forensic web services. Fourth Annual IFIP WG 11.9 Conference on Digital Forensics.

Gunestas, M., Mehmet, M., & Wijesekera, D. (2010). Detecting illegal business schemes in choreographed web services: The Ponzi/Pyramidal case. Sixth Annual IFIP WG 11.9 Conference on Digital Forensics.

Jacobs, L., & Wyss, R. (2003). KDPrevent: White paper: intelligent detection of money laundering and other financial crimes.

KDLabs Reports 2003. Kunz, B., & Peter, S. (2004). KDPrevent: detecting money laundering activities. KDLabs Reports 2004.

Liu, X., Zhang, P., & Zeng, D. (2008). Sequence matching for suspicious activity detection in anti-money laundering. Proceedings of the IEEE ISI 2008 PAISI, PACCF, and SOCO.

Mehmet, M., & Wijesekera, D. (2010). Ontological constructs to create money laundering schemes. Semantic Technologies for Intelligence, Defense, and Security Year 2010.

Mehmet, M., & Wijesekera, D. (2013). Detecting the evolution of money laundering schemes. Ninth Annual IFIP WG 11.9 Digital Forensics.

Schwartz, D., & Rouselle, T. (2008). Using social network analysis to target criminal networks. Trends in Organized Crime Year 2008.

Senator, T., Goldberg, H., Wooton, J., Cottini, A., Umar, A., Klinger, C., Llamas, W., …Wong, R. (1995). The FinCEN artificial intelligence system: Identifying potential money laundering from reports of large cash transactions. The 7th Conference on Innovative Applications of AI.

Senator, T., & Goldberg, H. (1996). Restructuring databases for knowledge discovery by consolidation and link formation. The Second International Conference on Knowledge Discovery and Data Mining.

Senator, T., Goldberg, H., & Wong, R. (1998). Restructuring transactional data for link analysis in the FinCEN AI system. AAAI Technical Report FS-98-01.

StreamBase. (2012). Powerful real-time architecture for today’s high performance modern intelligence systems. Federal Government, Defense, and Intelligence applications of Year 2012.

StreamBase. (2013). StreamSQL Guide. Retrieved from http://www.streambase.com/streamsql/.

Wasserman, S., & Faust, K. (1994). Chapters 1, 2, and 13. In Social Network Analysis: Methods and Applications. Cambridge University Press, London.



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