10. FINANCIAL FRAUD RECOGNITION AND IDENTIFICATION METHOD USING REASONING AND QUANTITATIVE ASSOCIATION EVALUATION
This work presents, a tool and a method dedicated for financial fraud recognition and evaluation based on stream of transactional data from financial institutions. Presented novel approach aims at context-aware data processing coming from ontology application and reasoning capabilities utilising DL and FOL (BAADER et al. 2003; STAAB et al., 2004) reasoners. Domain terminology defines elemental concepts, relations and classification rules which provide semantic processing capabilities. This article summarizes the proof of implementation of the concept of the IAFEC Ontology Toolkit for identifying fraud based on a set of problem solving ontologies. Methods, algorithms and software are inputs to IAFEC analytical tools demonstrating semantic-aware association analysis. A novelty in this approach is the inclusion of heterogeneous data analysis, which combine various layers of data extending range of available associations between individuals, organisations and financial market actors. Rich domain descriptions provide multiple ways of expressing relations in families, social groups, organisations, financial transactions and other dependencies. Progress in automatic reasoning and the availability of semantic processing tools (e.g. Protégé, Jena) encourage analyst to extend existing link analysis methods to contextual knowledge processing. Presented research provides, a high level insight into the analytical method and algorithms, which are based on logical reasoning, identification and ranking of associations found in financial transactions supplemented with intelligence data. Elaborated method is delivered as standalone desktop application integrated with Protégé OWL 5.0 (PROTÉGÉ WEB, 2016) reasoners and data integration services. Tool’s architecture simplifies integration with available semantic processing plug-ins while delivering functionalities for analytical process definition.
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- AuthorMariusz Chmielewski et al.
- AffiliationMilitary University of Technology Warsaw
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