Insurance Fraud Management as an Integrated Part of Business Intelligence Framework
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.
[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/.
[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/.
@article{"International Journal of Business, Human and Social Sciences:60003", author = "Pavel Pešout and Miroslav Andrle", title = "Insurance Fraud Management as an Integrated Part of Business Intelligence Framework", 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", volume = "5", number = "11", pages = "1615-6", }
