Money Laundering Evolution Detection, Transaction Scoring, and Prevention Framework




Mehmet, Murad Latif

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Money laundering is a major and ongoing global issue that has not been addressed with a dynamic approach by authorities using multiple systems. Made powerful by modern tools and resources available to them, money launderers are adopting more sophisticated schemes, spanning across many countries, to avoid being detected by anti-money laundering systems. Consequently, money laundering detection and prevention techniques must be multi-layered, multi-method, and multi-component to be ahead of the evolving laundering schemes. Handling such a multifaceted problem involves a large amount of unstructured, semi-structured and transactional data that stream at speeds requiring a high level of analytical processing to discover unraveling business-complexities, and discover deliberately concealed relationships. Therefore, I developed the money laundering evolution detection framework (MLEDF) to capture the trail of the dynamic and evolving schemes. My framework uses sequence matching, case-based analysis, social network analysis, and more importantly, complex event processing to link the fraud trails. My system capture a single scheme as an event in a trail in "real-time", and then using detection algorithms, associate the captured event with other ongoing events. A comprehensive Anti Money Laundering system must incorporate a risk modeling that calculates the dynamic attributes of transactional relationships and the potential social relationships among seemingly unrelated entities from a financial perspective. Therefore, I developed an industry-wide system to assign a risk scores to any transaction for being a part of a larger money laundering scheme. This score should be valid across every financial domain, continuously updated, and it is not specific to the evaluating financial institution. Additionally, I developed a transaction scoring exchange and money laundering prevention framework that uses a transaction messaging system and assigns scores to the transactions, where the score is derived from the dynamics risk of the transaction and the statically computed risk score. The transaction score is correlated to the static and dynamic risk scores, in order to identify transactions score pertaining to money laundering, and to prevent transaction sequences from being executed. The transaction score uses dynamic risk scores obtained from the analytical results of the real-time detection algorithms to produce valid results. My money laundering prevention system relies upon the finding of an accurate detection system, supported by dynamic risk modeling systems for transaction scoring. My prevention framework includes a protocol to exchange the information among the framework participants, and it incorporates two levels of cooperation and information sharing. The developed three level systems in this study consist of multi-levels and multi-components, and they can be easily incorporated within existing structure financial institutions. My system allows financial investigators to overcome the long processes and time-consuming characteristics of their investigations, to prevent money laundering schemes, or at least be aware of such schemes in their early stages. I validated the accuracy of calculating the money laundering evolution detection framework, dynamic risk scoring, and transactions scoring framework using a multi-phase test methodology. My test used data generated from real-life cases, and extrapolated to generate more varying scenarios of money laundering evolution and risk data from real-life schemes and patterns generator that I implemented.



Money Laundering Prevention Framework, Money Laundering Transaction Scoring, Anti Money Laundering Framework, Money Laundering Detection Framework, Money Laundering Dynamic Evolution, Money Laundering Dynamic Risk Model