A semantic rule based digital fraud detection

Mansoor Ahmed; Kainat Ansar; Cal B Muckley; Abid Khan; Adeel Anjum; Muhammad Talha

doi:10.7717/peerj-cs.649

A semantic rule based digital fraud detection

PeerJ Comput Sci. 2021 Aug 3:7:e649. doi: 10.7717/peerj-cs.649. eCollection 2021.

Authors

Mansoor Ahmed^{1

2}, Kainat Ansar¹, Cal B Muckley³, Abid Khan⁴, Adeel Anjum¹, Muhammad Talha¹

Affiliations

¹ Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan.
² Innovation Value Institute, Maynooth University, Maynooth, Ireland.
³ UCD College of Business and Geary Institute, Dublin, Ireland.
⁴ Department of Computer Science, Aberystwyth University, Aberystwyth, UK.

Abstract

Digital fraud has immensely affected ordinary consumers and the finance industry. Our dependence on internet banking has made digital fraud a substantial problem. Financial institutions across the globe are trying to improve their digital fraud detection and deterrence capabilities. Fraud detection is a reactive process, and it usually incurs a cost to save the system from an ongoing malicious activity. Fraud deterrence is the capability of a system to withstand any fraudulent attempts. Fraud deterrence is a challenging task and researchers across the globe are proposing new solutions to improve deterrence capabilities. In this work, we focus on the very important problem of fraud deterrence. Our proposed work uses an Intimation Rule Based (IRB) alert generation algorithm. These IRB alerts are classified based on severity levels. Our proposed solution uses a richer domain knowledge base and rule-based reasoning. In this work, we propose an ontology-based financial fraud detection and deterrence model.

Keywords: Alert model; Database; Digital fraud; Knowledge base; Semantic web.

Grants and funding

This research has been supported by European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 801522, by Science Foundation Ireland and co-funded by the European Regional Development Fund through the ADAPT Centre for Digital Content Technology grant number 13/RC/2106 P2 and Cal Muckley acknowledges the support of Science Foundation Ireland under Grant Numbers 16/SPP/3347 and 17/SP/5447. There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.