Abstract
Deterring money laundering in a technically integrated world requires a system built to exchange information and prevent the execution of complex schemes. Such a system requires industry specific standards to monitor, detect, score, and prevent money laundering schemes. Such a system requires a multi-level and multi-component framework, and must be implanted in the financial organizations. An effective prevention system relies upon the finding of an accurate detection system with a risk score assigned to each transaction dynamically. We propose a risk model that assigns a risk value for every transaction for the potential of being a part of a money laundering scheme. Our system uses the static risk score given by financial institutions. In addition, we continuously recalculate the static risk score of an entity based on the shared risk scores. We validated the accuracy of static risk scoring and transactions scoring using a multi-phases test methodology based on data generated from real-life money laundering cases.
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© 2014 Springer International Publishing Switzerland
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Mehmet, M., Güneştaş, M., Wijesekera, D. (2014). Dynamic Risk Model of Money Laundering. In: Bauer, T., Großmann, J., Seehusen, F., Stølen, K., Wendland, MF. (eds) Risk Assessment and Risk-Driven Testing. RISK 2013. Lecture Notes in Computer Science(), vol 8418. Springer, Cham. https://doi.org/10.1007/978-3-319-07076-6_1
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DOI: https://doi.org/10.1007/978-3-319-07076-6_1
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