OntoFiC : an ontology for financial fraud detection and customer behavior modeling
Lylia Abrouk, Hamza Chergui, Hamid Ahaggach · 2023
Fraud detection is a complex issue for financial institutions. They must have tools for the prevention and detection of fraud. In this article, we present our approach to detect fraudulent transactions in SWIFT network based on the domain ontology. Firstly, we present the OntoFiC ontology constructed for the modeling of SWIFT transactions and actors. This ontology is populated with a real dataset. We developed our rules-based approach with rules associated to fraud scenarios to label our transactions as legitimate or fraudulent. Finally, we made SPARQL requests to visualize these transactions through graphs. Our work is part of a collaboration project with a financial company, SKAIZen Group.