Fraud Detection and Fraudulent Risks Management in the Insurance Sector Using Selected Data Mining Tools

Leonard Mushunje

doi:doi:10.11648/j.ajdmkd.20190402.13

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Fraud Detection and Fraudulent Risks Management in the Insurance Sector Using Selected Data Mining Tools

Leonard Mushunje

Published in American Journal of Data Mining and Knowledge Discovery (Volume 4, Issue 2)

Received: 2 October 2019 Accepted: 6 November 2019 Published: 19 November 2019

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Abstract

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.

Published in	American Journal of Data Mining and Knowledge Discovery (Volume 4, Issue 2) This article belongs to the Special Issue Wider Thoughts on the Application of Data Mining Tools and Predictive Modelling in Finance
DOI	10.11648/j.ajdmkd.20190402.13
Page(s)	70-74
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Fraud, Data Mining, Insurance Sector, Knowledge Discovery

References

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APA Style

Leonard Mushunje. (2019). Fraud Detection and Fraudulent Risks Management in the Insurance Sector Using Selected Data Mining Tools. American Journal of Data Mining and Knowledge Discovery, 4(2), 70-74. https://doi.org/10.11648/j.ajdmkd.20190402.13

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ACS Style

Leonard Mushunje. Fraud Detection and Fraudulent Risks Management in the Insurance Sector Using Selected Data Mining Tools. Am. J. Data Min. Knowl. Discov. 2019, 4(2), 70-74. doi: 10.11648/j.ajdmkd.20190402.13

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AMA Style

Leonard Mushunje. Fraud Detection and Fraudulent Risks Management in the Insurance Sector Using Selected Data Mining Tools. Am J Data Min Knowl Discov. 2019;4(2):70-74. doi: 10.11648/j.ajdmkd.20190402.13

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@article{10.11648/j.ajdmkd.20190402.13,
  author = {Leonard Mushunje},
  title = {Fraud Detection and Fraudulent Risks Management in the Insurance Sector Using Selected Data Mining Tools},
  journal = {American Journal of Data Mining and Knowledge Discovery},
  volume = {4},
  number = {2},
  pages = {70-74},
  doi = {10.11648/j.ajdmkd.20190402.13},
  url = {https://doi.org/10.11648/j.ajdmkd.20190402.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajdmkd.20190402.13},
  abstract = {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.},
 year = {2019}
}

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TY  - JOUR
T1  - Fraud Detection and Fraudulent Risks Management in the Insurance Sector Using Selected Data Mining Tools
AU  - Leonard Mushunje
Y1  - 2019/11/19
PY  - 2019
N1  - https://doi.org/10.11648/j.ajdmkd.20190402.13
DO  - 10.11648/j.ajdmkd.20190402.13
T2  - American Journal of Data Mining and Knowledge Discovery
JF  - American Journal of Data Mining and Knowledge Discovery
JO  - American Journal of Data Mining and Knowledge Discovery
SP  - 70
EP  - 74
PB  - Science Publishing Group
SN  - 2578-7837
UR  - https://doi.org/10.11648/j.ajdmkd.20190402.13
AB  - 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.
VL  - 4
IS  - 2
ER  -

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Author Information

Leonard Mushunje

Department of Applied Mathematics and Statistics, Midlands State University, Gweru, Zimbabwe

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Plain Text BibTeX RIS

APA Style

Leonard Mushunje. (2019). Fraud Detection and Fraudulent Risks Management in the Insurance Sector Using Selected Data Mining Tools. American Journal of Data Mining and Knowledge Discovery, 4(2), 70-74. https://doi.org/10.11648/j.ajdmkd.20190402.13

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ACS Style

Leonard Mushunje. Fraud Detection and Fraudulent Risks Management in the Insurance Sector Using Selected Data Mining Tools. Am. J. Data Min. Knowl. Discov. 2019, 4(2), 70-74. doi: 10.11648/j.ajdmkd.20190402.13

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AMA Style

Leonard Mushunje. Fraud Detection and Fraudulent Risks Management in the Insurance Sector Using Selected Data Mining Tools. Am J Data Min Knowl Discov. 2019;4(2):70-74. doi: 10.11648/j.ajdmkd.20190402.13

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@article{10.11648/j.ajdmkd.20190402.13,
  author = {Leonard Mushunje},
  title = {Fraud Detection and Fraudulent Risks Management in the Insurance Sector Using Selected Data Mining Tools},
  journal = {American Journal of Data Mining and Knowledge Discovery},
  volume = {4},
  number = {2},
  pages = {70-74},
  doi = {10.11648/j.ajdmkd.20190402.13},
  url = {https://doi.org/10.11648/j.ajdmkd.20190402.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajdmkd.20190402.13},
  abstract = {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.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Fraud Detection and Fraudulent Risks Management in the Insurance Sector Using Selected Data Mining Tools
AU  - Leonard Mushunje
Y1  - 2019/11/19
PY  - 2019
N1  - https://doi.org/10.11648/j.ajdmkd.20190402.13
DO  - 10.11648/j.ajdmkd.20190402.13
T2  - American Journal of Data Mining and Knowledge Discovery
JF  - American Journal of Data Mining and Knowledge Discovery
JO  - American Journal of Data Mining and Knowledge Discovery
SP  - 70
EP  - 74
PB  - Science Publishing Group
SN  - 2578-7837
UR  - https://doi.org/10.11648/j.ajdmkd.20190402.13
AB  - 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.
VL  - 4
IS  - 2
ER  -

Copy | Download