Money Laundering Analytics Based on Contextual Analysis. Application of Problem Solving Ontologies in Financial Fraud Identification and Recognition
Advances in automatic reasoning and the availability of semantic processing tools encourage operational specialist to extend existing link analysis methods towards contextual data awareness. In this paper we summarise a proof of concept implementation of IAFEC Ontology Toolkit for financial fraud identification based on set of problem solving ontologies. The method, algorithms and software is a contribution for IAFEC analytical tools demonstrating semantic-aware association analysis. The novelty in such approach comes from incorporating heterogeneous types of data which usually are processed by graph or network methods. The development of semantic tools, extend capabilities of graph-based approach by delivering indirect association identification as well as methods for inference path explanation. Presented material provides high level view of the method and analytical algorithms which rely on logic reasoning and semantic association identification and ranking. Developed method has been implemented as a standalone java application integrated within Protégé OWL 5.0. Such characteristic allows for further extensions and usage as a part of processing flow utilising ontology processing tools.
KeywordsFinancial fraud identification Data mining Knowledge discovery Semantic association Ontologies Context-aware processing
This work was partially supported by supported by the National Center For Research and Development research projects DOBR/0073/R/ID1/2012/03: “Advanced ICT techniques supporting data analysis processes in the domain of financial frauds” and internal grant WAT/RMN-948 “Sensor data fusion methods utilizing semantic models and artificial intelligence methods”.
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