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Title |
An Ontology-Based Alert Model for Financial Fraud Detection |
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Author |
Kainat Ansar, Mansoor Ahmed, Abid Khan |
| Citation |
Vol. 25 No. 6 pp. 142-158 |
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Abstract |
The objective of this research is to develop an effective model to detect financial fraud for the sustainable development of banks and financial institutes. Financial fraud is an issue which has a deep impact on the ordinary consumer as well as the finance industry. Our dependence on internet banking has grown far beyond our imagination and has made this problem more compound. Financial sector all over the world shows significant improvements in fraud detection. Fraud detection is a reactive response to misappropriation of financial results, which causes incurring cost that may or may not be recoverable due to fraud that has already occurred. However, the problem for automatic fraud deterrence is still a challenging task. Our focus in this work is on fraud deterrence. Deterrence is a proactive, preventative measure, which prevents loss before happening. We have proposed an ontology-based alert model for money laundering deterrence. We have also proposed an Intimation Rule Based (IRB) alert generation algorithm which stops fraud before it happens. This article first introduces the data representation model (ontologies) and the advantages of using ontologies over databases. Then we briefly discuss our proposed methodology and system working of our ontology-based alert model. We also evaluate our ontology using OntoClean methodology and compare results with existing techniques. Finally, the comparison results show that our system outperforms the existing systems. |
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Keywords |
Ontology-Based Alert, Fraud Detection; Ontology; Suspicious Transactions; Alert Model; Knowledge Base; Jena |
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