The development of an effective mechanism to detect suspicious transactions is a critical problem for financial institutions in their endeavor to prevent anti-money laundering activities. This research addresses this problem by proposing an ontology based expert-system for suspicious transaction detection. The ontology consists of domain knowledge and a set of (SWRL) rules that together constitute an expert system. The native reasoning support in ontology is used to deduce new knowledge from the predefined rules about suspicious transactions. The presented expert-system has been tested on a real data set of more than 8 million transactions of a commercial bank. The novelty of the approach lies in the use of ontology driven technique that not only minimizes the data modeling cost but also makes the expert-system extendable and reusable for different applications.
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