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.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
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