In this research, the most modern and seemingly least understood form of money laundering, trade-based money laundering is introduced. As our world rarely presents itself in ways that are possible to dissect using simple “Yes/Truth” and “No/False” variables, fuzzy mathematics offers us a way to deal with uncertainty: something largely inherent in the world of money laundering. The framework for a fuzzy application for case detection in trade-based money laundering is presented. Basic concepts of fuzzy mathematics are summarized and the basis of a decision support system is laid out in an attempt to aid those working in detection. A case study is analyzed, linguistic variables extracted and a linguistic fuzzy rule base is formed. The functioning of the rule base is explained and ways for validation are covered. Data collection in this field of study is of extra challenging nature. Therefore dummy data is used. Further research intends to substitute the current variables with expert knowledge input.
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