Communication Papers of the 17th Conference on Computer Science and Intelligence Systems

DOI: http://dx.doi.org/10.15439/2022F35

Citation: Communication Papers of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 32, pages 277–285 (2022)

Full text

Abstract. Financial Frauds have dynamically changed, the fraudsters are becoming more sophisticated.There has been an estimated global loss of 5.127 trillion each year due to various forms of financial frauds. Industries like banking, insurance, e-commerce and telecommunication are the main victims of financial frauds. Several techniques have been proposed and applied to understand and detect financial frauds. In this paper we propose an ontology to describe financial frauds and related knowledge. The aim of this ontology is to provide a semantic framework for the detection of financial frauds. Theoretical ontology has been elaborated exploring various sources of information. After describing the research objectives, related works and research methodology, this paper presents details of theoretical ontology. It is followed by its validation using real data sets. Discussion of the obtained results gives some perspectives for the future work.

References

1. Panos Alexopoulos, Kostas Kafentzis, Xanthi Benetou, Tassos Tagaris, and Panos Georgolios. Towards a generic fraud ontology in e- government. In ICE-B, pages 269–276, 2007.
2. Gonçalo Carnaz, Vitor Nogueira, and Mário Antunes. Ontology-based framework applied to money laundering investigations. In Proceedings of the Seventh Conference on Informatics at the University of Evora, pages 1–17. University of Evora Evora, 2017.
3. Helena Dudycz and Jerzy Korczak. Conceptual design of financial ontology. In 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), pages 1505–1511. IEEE, 2015.
4. Giannis Potamitis. Design and implementation of a fraud detection expert system using ontology-based techniques. Univ. Manchester, Manchester, UK, Tech. Rep. COMP60990, 2013.
5. Mansoor Ahmed, Kainat Ansar, Cal B Muckley, Abid Khan, Adeel Anjum, and Muhammad Talha. A semantic rule based digital fraud detection. PeerJ Computer Science, 7:e649, 2021.
6. Ali Ahmadian Ramaki, Reza Asgari, and Reza Ebrahimi Atani. Credit card fraud detection based on ontology graph. International Journal of Security, Privacy and Trust Management (IJSPTM), 1(5):1–12, 2012.
7. Steven L Skalak, Thomas W Golden, Mona M Clayton, and Jessica S Pill. A guide to forensic accounting investigation. John Wiley & Sons, 2011.
8. Johnson Olabode Adeoti. Automated teller machine (atm) frauds in nigeria: The way out. Journal of Social Sciences, 27(1):53–58, 2011.
9. Linda Delamaire, Hussein Abdou, and John Pointon. Credit card fraud and detection techniques: a review. Banks and Bank systems, 4(2):57–68, 2009.
10. S Benson Edwin Raj and A Annie Portia. Analysis on credit card fraud detection methods. In 2011 International Conference on Computer, Communication and Electrical Technology (ICCCET), pages 152–156. IEEE, 2011.
11. Michael Levi. Money laundering and its regulation. The Annals of the American Academy of Political and Social Science, 582(1):181–194, 2002.
12. Michael Levi and Peter Reuter. Money laundering. Crime and justice, 34(1):289–375, 2006.
13. Koen Kerremans, Yan Tang, Rita Temmerman, and Gang Zhao. Towards ontology-based e-mail fraud detection. In 2005 portuguese conference on artificial intelligence, pages 106–111. IEEE, 2005.