Fraud Detection and Fraudulent Risks Management in the Insurance Sector Using Selected Data Mining Tools
American Journal of Data Mining and Knowledge Discovery
Volume 4, Issue 2, December 2019, Pages: 70-74
Received: Oct. 2, 2019;
Accepted: Nov. 6, 2019;
Published: Nov. 19, 2019
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Leonard Mushunje, Department of Applied Mathematics and Statistics, Midlands State University, Gweru, Zimbabwe
Knowledge discovery, shortly known as Data mining plays a crucial role within the insurance sector. Serious troublesome cases such as fraudulent cases can be well managed in the insurance sector through data mining application. In this paper, we aim to put on surface the two forms of fraud that is softy and hard fraud, to give out the causes of such fraudulent acts and to state out different suggested data mining techniques that can be applied to the insurance data to detect fraud. Also, we aim to highlight other benefits that can be enjoyed from using data mining in the insurance sector. We conjectured and found that, application of data mining helps to quickly detect fraud, reduce operation cost and to improve profit margins and increased competitive advantages. We put forward that techniques such as association, clustering, classification and regression are good when detecting fraud from the insurance claims data and should be acquired and applied. We then recommended that, underwriters and insurance officials should contribute much in preventing fraudulent cases in the insurance sector. This is so because, prevention is better than cure. Above all, we concluded that, application of data mining techniques through sequential pattern mining can help much to predict any future and potential fraudulent cases. This is helpful on planning and to keep the insurers alert before the fraudulent risk occurs.
Fraud Detection and Fraudulent Risks Management in the Insurance Sector Using Selected Data Mining Tools, American Journal of Data Mining and Knowledge Discovery. Special Issue: Wider Thoughts on the Application of Data Mining Tools and Predictive Modelling in Finance.
Vol. 4, No. 2,
2019, pp. 70-74.
Copyright © 2019 Authors retain the copyright of this article.
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Sumiran, K (2018). An Overview of Data Mining Techniques and Their Application in Industrial Engineering. Asian Journal of Applied Science and Technology (AJAST). Open Access Quarterly International Journal) Volume 2, Issue 2, Pages 947-953.
Agrawal, R, Imielinski, T & Swami, A (1993). Mining Association Rules between Sets of Items in Large Databases. Proceedings Of The 1993 Acm Sigmod International Conference On Management Of Data, Washington Dc (Usa).
Torabi, A., KiaianMousavy, S., Dashti, V., Saeedi, M., &Yousefi, N. (2018). A New Prediction Model Based on Cascade NN for Wind Power Prediction. Computational Economics.
MOSTAFA, A (2016). Review of Data Mining Concept and its Techniques. DOI: 10.13140/RG.2.1.3455.2729. SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data, Pages 207-216.
Freitas, A (2013). Data Mining and Knowledge Discovery with Evolutionary Algorithms. Natural Computing Series.
Lijia Guo. “Applying Data mining Techniques in Property/Casualty Insurance”, Casualty Actuarial Society Forum Casualty Actuarial Society - Arlington, Virginia Winter 2003.
T. L. OshiniGoonetilleke and H. A. Caldera Mining Life Insurance Data for Customer Attrition Analysis, Journal of Industrial and Intelligent Information Vol. 1, No. 1, March 2013.
RekhaBhowmik, “Detecting Auto Insurance Fraud by Data Mining Techniques”, Journal of Emerging Trends in Computing and Information Sciences, Volume 2 No. 4, April 2011.
S. Balaji and Dr. K. Srinivasta, “Naïve Bayes Classification Approach for Mining Life Insurance Databases for Effective Prediction of Customer Preferences over Life Insurance Products”, International journal of Computer Applications, vol. 51, No. 3, pp. 22-26. August 2012.
H. Lookman Sithic, T. Balasubramanian, “Survey of Insurance Fraud Detection Using Data Mining Techniques”, International Journal of Innovative Technology and Exploring Engineering, Vol-2, Issue-3, February 2013.
Jayanthi Ranjan “Data mining in pharmacy sector: benefits”, IJHCQA, Vol. 22 No. 1, 2009, pp. 82-92.
Hajiadeh, E, Ardakani, H. D and Shahrabi, J, (2010). Application of data mining techniques in stock markets: A survey. Journal of Economics and International Finance, vol (2).
Boris, K and Evgenii, V, (2005). Data Mining for Financial Applications. Data Mining and Knowledge Discovery Handbookpp 1203-1224.
Berry, Michael J. A. danLinoff, Gordon S. 2004, Data Mining Techniques, 1st ed., John Wiley & Sons Inc., Indianapolis, Indiana.
Gigi De Vault, (2019). A Basic Statistics Approach to Analyzing Quantitative Data. available at https://www.thebalancesmb.com/what-is-simple-linear-regression-2296697.