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Insurance Fraud Management as an Integrated Part of Business Intelligence Framework

Authors: Pavel Pešout, Miroslav Andrle

Abstract:

Frauds in insurance industry are one of the major sources of operational risk of insurance companies and constitute a significant portion of their losses. Every reasonable company on the market aims for improving their processes of uncovering frauds and invests their resources to reduce them. This article is addressing fraud management area from the view of extension of existing Business Intelligence solution. We describe the frame of such solution and would like to share with readers all benefits brought to insurance companies by adopting this approach in their fight against insurance frauds.

Keywords: business intelligence, insurance fraud, fraudmanagement

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1076298

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References:


[1] Coalition Against Insurance Fraud. Available: http://www.insurancefraud.org.
[2] CEA Insurers of Europe. Available: http://www.cea.eu.
[3] P. Ponniah, Data Warehousing Fundamentals for IT Professionals. Wiley, 2010.
[4] B. Inmon, Building the Data Warehouse. Wiley and Sons, 1992.
[5] C. Howson, Successful Business Intelligence: Secrets to Making BI a Killer App. McGraw-Hill Osborne Media, 2007.
[6] Gartner Inc. Available: http://www.gartner.com.
[7] S. Williams, N. Williams, The Profit Impact of Business Intelligence. Morgan Kaufmann, 2006.
[8] European Commission, Solvency II. Available: http://ec.europa.eu/internal_market/insurance/solvency/.
[9] W. K. Wilhelm, "The Fraud Management Lifecycle Theory: A Holistic Approach to Fraud Management", in Journal of Economic Crime Management, Vol. 2, Issue 2, 2004.
[10] UK Financial Services Authority, Firms- High-Level Management of Fraud Risk, 2006. Available: www.fsa.gov.uk.
[11] R. G. Eccles, M. P. Krzus, D. Tapscott, One Report: Integrated Reporting for a Sustainable Strategy. Wiley, 2010.
[12] S. Chaudhuri, U. Dayal, "An Overview of Data Warehousing and OLAP Technology", ACM SIGMOD Record, Vol. 26, Issue 1, 1997, pp. 65- 74.
[13] G. Hawkins, Customer Intelligence. Breezy Heights Publishing, 2003.
[14] T. Roberts, "Improving the Defense Lines: The Future of Fraud Detection in the Insurance Industry (with Fraud Risk Models, Text Mining, and Social Networks)", in SAS Global Forum 2010.
[15] S. Sarsfield, The Data Governance Imperative. IT Governance Publishing, 2009.
[16] P. L. Brockett, R. A. Derrig, L. L. Golden, A. Levine, M. Alpert, "Fraud Classification Using Principal Component Analysis of RIDITs", in The Journal of Risk and Insurance, Vol. 69, No. 3, 2002, pp. 341-371.
[17] S. Kudyba, Managing data mining: advice from experts. Idea Group Inc, 2004.
[18] M. Samociuk, N. Iyer, H. Doody, A Short Guide to Fraud Risk. Farnham: Gower Publishing, 2010.
[19] The IIA, Practice Advisory 1210.A2-2. Auditor-s Responsibilities Relating to Fraud Investigation, Reporting, Resolution, and Communication. Available: www.theiia.org/.