Fraud Detection in Healthcare System using Symbolic Data Analysis
Sahana Munavalli1, Sanjeevakumar M. Hatture2
1Sahana Munavalli*, Department of Computer Science and Engineering, Basaveshwar Engineering College, Bagalkot (Karnataka), India.
2Sanjeevakumar M. Hatture, Department of Computer Science and Engineering, Basaveshwar Engineering College, Bagalkot Under Visvesvaraya Technological University, Belagavi (Karnataka), India.
Manuscript received on June 20, 2021. | Revised Manuscript received on June 30, 2021. | Manuscript published on July 30, 2021. | PP: 1-7 | Volume-10, Issue-9, July 2021 | Retrieval Number: 100.1/ijitee.H92690610821 | DOI: 10.35940/ijitee.H9269.0710921
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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: In the era of digitization the frauds are found in all categories of health insurance. It is finished next to deliberate trickiness or distortion for acquiring some pitiful advantage in the form of health expenditures. Bigdata analysis can be utilized to recognize fraud in large sets of insurance claim data. In light of a couple of cases that are known or suspected to be false, the anomaly detection technique computes the closeness of each record to be fake by investigating the previous insurance claims. The investigators would then be able to have a nearer examination for the cases that have been set apart by data mining programming. One of the issues is the abuse of the medical insurance systems. Manual detection of frauds in the healthcare industry is strenuous work. Fraud and Abuse in the Health care system have become a significant concern and that too inside health insurance organizations, from the most recent couple of years because of the expanding misfortunes in incomes, handling medical claims have become a debilitating manual assignment, which is done by a couple of clinical specialists who have the duty of endorsing, adjusting, or dismissing the appropriations mentioned inside a restricted period from their gathering. Standard data mining techniques at this point do not sufficiently address the intricacy of the world. In this way, utilizing Symbolic Data Analysis is another sort of data analysis that permits us to address the intricacy of the real world and to recognize misrepresentation in the dataset.
Keywords: Data mining, Health insurance, Fraud detection, Anomaly detection, Symbolic data.