International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 7, Issue 4 (July-August 2025) Submit your research before last 3 days of August to publish your research paper in the issue of July-August.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 7 Issue 4
July-August 2025

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Proactive Fraud Prevention in Healthcare and Retail: Leveraging Deep Learning for Early Detection and Mitigation of Malicious Practices

Author(s) Esther Makandah, Wycliff Nagalia
Country United States
Abstract Fraudulent activities in both healthcare and retail sectors continue to pose serious economic, ethical, and operational threats, costing billions annually and undermining public trust and institutional efficiency. With the digital transformation of service delivery and payment mechanisms, the complexity and scale of fraud have evolved, requiring more intelligent, adaptable, and proactive countermeasures. This study explores the transformative potential of deep learning techniques in proactively detecting and mitigating fraudulent behavior across these two critical industries. From a broader perspective, the converging vulnerabilities shared by healthcare and retail domains, including false billing, inventory manipulation, claim inflation, identity theft, and transactional anomalies was examined. The study revealed that deep learning-based systems, including architectures such as autoencoders, recurrent neural networks (RNNs), long short-term memory (LSTM) models, and graph neural networks (GNNs), offer superior capability in recognizing subtle, evolving fraud schemes. These models, when integrated into real-time monitoring pipelines, enable early detection and dynamic response with reduced false positives. However, ethical, regulatory, and technical challenges, includes data privacy, algorithmic transparency, and integration into existing workflows.
Keywords Anomaly detection, Artificial intelligence, Deep learning, Healthcare fraud, Retail analytics
Published In Volume 7, Issue 3, May-June 2025
Published On 2025-06-25
DOI https://doi.org/10.36948/ijfmr.2025.v07i03.48118
Short DOI https://doi.org/g9rnwv
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 7 Isu 3Cover Page Vol 7 Isu 2Cover Page Vol 7 Isu 1

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijfmr.com