Fraud: The Affinity of Classification Techniques to Insurance Fraud Detection
Saliu Adam Muhammad
Saliu Adam Muhammad, Department of Computer Application, Information Science and Engineering, Technology, Hunan University, Changsha, Hunan Province, P.R. China.
Manuscript received on 6 April 2014 | Revised Manuscript received on 17 April 2014 | Manuscript Published on 30 April 2014 | PP: 62-66 | Volume-3 Issue-11, April 2014 | Retrieval Number: K15670431114/14©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Quite a large number of data mining techniques employed in financial fraud detection (FFD) are seen to be classification techniques. In this paper, we developed an algorithm to find the features of classification techniques (or method) that so much place it (classification techniques) in the heart of researchers in their various efforts in the study of insurance frauds detection. We also got to know the characteristics of insurance frauds data that made data mining classification techniques so much attracted to it (insurance data).
Keywords: Affinity, Classification Techniques, Insurance Frauds Common Features.
Scope of the Article: Classification