UGC Approved Journal no 63975

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 8 Issue 7
July-2021
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2107546


Registration ID:
312977

Page Number

e313-e323

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Title

Symbolic Data Approach For Fraud Detection In Healthcare System

Abstract

Fraud and abuse in the health care system have become a significant concern and that too inside health insurance organizations. In the last decade due to 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 fraud in the healthcare data. In the era of digitization, the frauds are found in all categories of health insurance. It is finished next to deliberate trickiness or fraud 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 fraud 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 in healthcare system claims is the abuse of the medical insurance. Manual detection of frauds in the healthcare industry is strenuous work. In the proposed work the symbolic data object representation reduces the complexity of the medical data and machine learning and symbolic data analysis recognize the fraud detection. The symbolic data approach for fraud detection in healthcare system achieves promising results and can be extended to other healthcare systems.

Key Words

Fraud detection; Anomaly detection; Data mining; Health insurance; Symbolic data

Cite This Article

"Symbolic Data Approach For Fraud Detection In Healthcare System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 7, page no.e313-e323, July-2021, Available :http://www.jetir.org/papers/JETIR2107546.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Symbolic Data Approach For Fraud Detection In Healthcare System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 7, page no. ppe313-e323, July-2021, Available at : http://www.jetir.org/papers/JETIR2107546.pdf

Publication Details

Published Paper ID: JETIR2107546
Registration ID: 312977
Published In: Volume 8 | Issue 7 | Year July-2021
DOI (Digital Object Identifier):
Page No: e313-e323
Country: Bagalkot, Karnataka, India .
Area: Engineering
ISSN Number: 2349-5162


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