publication . Preprint . 2013

Survey of Insurance Fraud Detection Using Data Mining Techniques

Sithic, H. Lookman; Balasubramanian, T.;
Open Access English
  • Published: 03 Sep 2013
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 ...
ACM Computing Classification System: ComputingMilieux_COMPUTERSANDSOCIETYComputingMilieux_LEGALASPECTSOFCOMPUTING
free text keywords: Computer Science - Other Computer Science
Related Organizations
Download from

M.K.Das Law officer, the New india Assurance Co.Ltd., Published in The Insurance Times , November , 2008.

Anuj Sharma, Prabin Kumar Panigrahi “A Review of Financial Accounting Fraud Detection based on Data Mining Techniques “ international journal of computer applications , volume 39-No.1, February 2012.

Turban, E,. Aronson, J.E., Liang, T.P., & Sharda, R. (2007). Decision Support and Business Intelligence Systems, Eighth edition, Pearson Education, 2007.

Zhang,D., & Zhou, L.(2004). Discovering golden nuggest: data mining in financial application, IEEE Transactions on systems, Man and Cybernetics 34(4)(2004) Nov.

Ahmed ,S.R.(2004). Applications of data mining in retail business, international conference on information Technology: coding and computing 2 (2) (2004) 455-459.

Yamanishi, K., Takeuchi, J.,Williams, G., & Milne,P.(2004). On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms, Data Mining and Knowledge Discovery 8(3) (2004) 373-421.

Han , J., & Kamber, M (2006). Data Mining; Concepts and Techniques, second edition, Morgan Kaufmann Publishers , 2006, pp. 285-464.

Eick, S.G., & D.E.(1996). Visualizing corporate data, AT&T Technical Journal 75 (1) (1996) 74-86.

“Cluster Analysis for Anomaly Detection in Accounting Data: An Audit Approach” The International Journal of Digital Accounting Research vol. 11, 2011, pp. 69-84.

B.Manjula, S.S.V.N.Sarma, Dr.A.Govardhan & R.Lakshman Naik “DFFS: Detecting Fraud in Finance Sector “International Journal of Advanced Engineering Sciences and Technologies. Vol No.9, issue No.

Han J. and Kamber K., Data mining : Concepts and Techniques, San Fracisco: morgan Kaufmann Publishers , 2001.

H.Lookman Sithic, Received B.Sc Computer Science From Jamal Mohamed College, Bharathidasan University, Trichy 1999, M.S Information Technology From Jamal Mohamed College, Bharathidasan University, Trichy 2001, M.Phil Computer Science from Periyar University, Salem 2004 & Now he is pursuing his Ph.D Computer Science in Bharathiyar University from 2011.

He is having 11 Years of Teaching experience in Computer Science, currently he is working as an Associate.Professor Department of Computer Application, Muthayammal College of Arts & Science, Periyar University.

T.Balasubramanian, Received B.Sc Computer Science From Bishop heber College, Bharathidasan University, Trichy 1995, M.Sc Computer Science From Jamal Mohamed College, Bharathidasan University, Trichy 1997, M.Phil Computer Science from Periyar University, Salem 2007 & Now he is pursuing his Ph.D Computer Science in Bharathiyar University from 2009. Currently he is working as HOD Cum Asst.Professor Department of Computer Science, Sri Vidya Mandir Arts & Science College.

Powered by OpenAIRE Research Graph
Any information missing or wrong?Report an Issue