Survey of Insurance Fraud Detection using Data Mining Techniques
H. Lookman Sithic1, T. Balasubramanian2
1H. Lookman sithic, Associate. Professor Department of Computer Applications, Periyar University, Muthayammal College of Arts & Science, Namakkal, India.
2T. Balasubramanian, HOD Cum Asst. Professor, Department of Computer Sciences, Periyar University/ Sri Vidya Mandir Arts and Science College/ Krishnagiri, India.
Manuscript received on 07 February 2013 | Revised Manuscript received on 21 February 2013 | Manuscript Published on 28 February 2013 | PP: 62-65 | Volume-2 Issue-3, February 2013 | Retrieval Number: C0422022313/2013©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© 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: With an increase in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection has become an emerging topics of great importance for academics, research and industries. Financial fraud is a deliberate act that is contrary to law, rule or policy with intent to obtain unauthorized financial benefit and intentional misstatements or omission of amounts by deceiving users of financial statements, especially investors and creditors. Data mining techniques are providing great aid in financial accounting fraud detection, since dealing with the large data volumes and complexities of financial data are big challenges for forensic accounting. Financial fraud can be classified into four: bank fraud, insurance fraud, securities and commodities fraud. Fraud is nothing but wrongful or criminal trick planned to result in financial or personal gains. This paper describes the more details on insurance sector related frauds and related solutions. In finance, insurance sector is doing important role and also it is unavoidable sector of every human being.
Keywords: Insurance, Data mining, Hard fraud, Soft fraud, Financial fraud.
Scope of the Article: Data mining