Fraud Detection in Health Insurance Claims using Machine Learning Algorithms
D. Vineela1, P. Swathi2, T. Sritha3, K. Ashesh4
1D. Vineela, Department of CSE, Koneru Lakshmaiah Education Foundation.
2P. Swathi, Department of CSE, Koneru Lakshmaiah Education Foundation.
3T. Sritha, Department of CSE, Koneru Lakshmaiah Education Foundation.
4K. Ashesh, Department of CSE, Koneru Lakshmaiah Education Foundation.
Manuscript received on January 01, 2020. | Revised Manuscript received on January 20, 2020. | Manuscript published on January 30, 2020. | PP: 2999-3004 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6485018520/2020©BEIESP | DOI: 10.35940/ijrte.E6485.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Fraud can be spread broadly and it is extremely costly to the therapeutic protection framework. Unscrupulous protection might be a case created to cover up or twist information that is intended to deliver social insurance edges. Cheats might be of the numerous sorts and submitted by the protection guarantor or the safeguarded. The unscrupulous social insurance providers are the reason for extortion in the wellbeing segment. The commitment of this case misrepresentation discovery is Associate in nursing trial study on extortion recognizable proof and exploitative examples. Along these lines, to identify the misrepresentation information handling procedures are utilized. For the most part essential based oddities are implemented exploitation applied math call rules and k-means, rule based mining and affiliation rule bolstered appropriation calculations are applied. Through these abnormalities the extortion in certifiable information is recognized. Be that as it may, there might be a great deal of progress done by exploitation various information handling procedures. In this way the arranged methodology has been assessed basing on the protection information and furthermore the trial results from our methodology are efficient in human services misrepresentation. Other self-advancing misrepresentation location ways can likewise be applied on this protection information.
Keywords: Fraud Detection, Data Analysis, Clustering, Statistical Decision Rules.
Scope of the Article: Machine Learning.