Fraudulent Health Insurance Claims Detection using Machine Learning

Fraudulent Health Insurance Claims Detection using Machine Learning

Registration ID: IJNRD_215112

Published ID: IJNRD2403370

DOI: Click Here to Get

Harika Gudibandi , G.Mahi Durga Lakshmi , J.Pavaneeth , G.Bhargav Ram , Ch.Lalitha Syama Sundari

Health insurance fraud harm the integrity and long-term viability of global healthcare systems. With the expanding use of digital technology and electronic health records, there is a greater need for effective fraud detection tools to defend against financial losses and assure the quality of service. This review paper provides a comprehensive summary of current research on detecting false health insurance claims utilising machine learning approaches. This paper discusses a variety of methodology, including supervised and unsupervised learning algorithms, feature engineering techniques, anomaly detection methods, and ensemble learning approaches. It examines the issues of imbalanced datasets, noisy data, and model interpretability, as well as techniques for overcoming them.This research also assesses the effectiveness of machine learning models in detecting false health insurance claims utilizing real-world datasets and performance measurements such as accuracy, precision, recall, and F1 score. We hope that this poll will provide useful insights into the present status of research in this subject, as well as indicate future research directions to improve healthcare fraud detection systems.

Published Paper   E-Certificate

"Fraudulent Health Insurance Claims Detection using Machine Learning", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 3, page no.d524-d533, March-2024, Available :https://ijnrd.org/papers/IJNRD2403370.pdf

Volume 9 Issue 3, March-2024

Pages : d524-d533

Paper Reg. ID: IJNRD_215112

Published Paper Id: IJNRD2403370

Downloads: 000121214

Research Area: Engineering

Country: Guntur, Andhra Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2403370.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2403370

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

Call For Paper - Volume 10 | Issue 8 | August 2025

IJNRD is Scholarly open access journals, Peer-reviewed, and Refereed Journals, High Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool), Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) with Open-Access Publications.

INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to explore advances in research pertaining to applied, theoretical and experimental Technological studies. The goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world. IJNRD will provide an opportunity for practitioners and educators of engineering field to exchange research evidence, models of best practice and innovative ideas.

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Paper Submission Open For: August 2025

Current Issue: Volume 10 | Issue 8

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Frequency: Monthly (12 issue Annually).

Journal Type: International Peer-reviewed, Refereed, and Open Access Journal.

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