Framework for the identification of fraudulent health insurance claims using association rule mining
Deliberate cheating by concealing and omitting facts while claiming from health insurance providers is considered as one of fraudulent activities in the health insurance domain which has led to significant amount of monetary loss to the providers. In view of the above, careful scanning of the submit...
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Institute of Electrical and Electronics Engineers Inc.
2018
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my.utp.eprints.217732018-08-14T00:45:10Z Framework for the identification of fraudulent health insurance claims using association rule mining Kareem, S. Ahmad, R.B. Sarlan, A.B. Deliberate cheating by concealing and omitting facts while claiming from health insurance providers is considered as one of fraudulent activities in the health insurance domain which has led to significant amount of monetary loss to the providers. In view of the above, careful scanning of the submitted claim documents need to be conducted by the insurance companies in order to spot any discrepancy that indicates fraud. For this purpose, manual detection is neither easy nor practical as the claim documents received are plentiful and for diverse medical treatments. Hence, this paper shares the initial stage of our study which is aimed to propose an approach for detecting fraudulent health insurance claims by identifying correlation or association between some of the attributes on the claim documents. With the application of a data mining technique of association rules, this study advocates that the successful determination of correlated attributes can adequately address the discrepancies of data in fraudulent claims and thus reduce fraud in health insurance. © 2017 IEEE. Institute of Electrical and Electronics Engineers Inc. 2018 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047435296&doi=10.1109%2fICBDAA.2017.8284114&partnerID=40&md5=32f134dd6a775decec634b9c90eb8b70 Kareem, S. and Ahmad, R.B. and Sarlan, A.B. (2018) Framework for the identification of fraudulent health insurance claims using association rule mining. 2017 IEEE Conference on Big Data and Analytics, ICBDA 2017, 2018-J . pp. 99-104. http://eprints.utp.edu.my/21773/ |
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Deliberate cheating by concealing and omitting facts while claiming from health insurance providers is considered as one of fraudulent activities in the health insurance domain which has led to significant amount of monetary loss to the providers. In view of the above, careful scanning of the submitted claim documents need to be conducted by the insurance companies in order to spot any discrepancy that indicates fraud. For this purpose, manual detection is neither easy nor practical as the claim documents received are plentiful and for diverse medical treatments. Hence, this paper shares the initial stage of our study which is aimed to propose an approach for detecting fraudulent health insurance claims by identifying correlation or association between some of the attributes on the claim documents. With the application of a data mining technique of association rules, this study advocates that the successful determination of correlated attributes can adequately address the discrepancies of data in fraudulent claims and thus reduce fraud in health insurance. © 2017 IEEE. |
| format |
Article |
| author |
Kareem, S. Ahmad, R.B. Sarlan, A.B. |
| spellingShingle |
Kareem, S. Ahmad, R.B. Sarlan, A.B. Framework for the identification of fraudulent health insurance claims using association rule mining |
| author_facet |
Kareem, S. Ahmad, R.B. Sarlan, A.B. |
| author_sort |
Kareem, S. |
| title |
Framework for the identification of fraudulent health insurance claims using association rule mining |
| title_short |
Framework for the identification of fraudulent health insurance claims using association rule mining |
| title_full |
Framework for the identification of fraudulent health insurance claims using association rule mining |
| title_fullStr |
Framework for the identification of fraudulent health insurance claims using association rule mining |
| title_full_unstemmed |
Framework for the identification of fraudulent health insurance claims using association rule mining |
| title_sort |
framework for the identification of fraudulent health insurance claims using association rule mining |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2018 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047435296&doi=10.1109%2fICBDAA.2017.8284114&partnerID=40&md5=32f134dd6a775decec634b9c90eb8b70 http://eprints.utp.edu.my/21773/ |
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