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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM62194
Volume:
10 |
4 |
April 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Artificial Intelligence and Data Science
United Institute of Technology
Artificial Intelligence and Data Science
United Institute of Technology

Artificial Intelligence and Data Science
United Institute of Technology

Artificial Intelligence and Data Science
United Institute of Technology

Artificial Intelligence and Data Science
United Institute of Technology
The rapid expansion of digital healthcare fundraising platforms has significantly improved access to financial assistance for patients facing critical medical conditions. Individuals can now share their medical needs online and receive support from a wide network of donors. However, this convenience has also introduced a serious challenge: the increasing prevalence of fraudulent medical fund requests. In many instances, users upload manipulated or fabricated medical documents, including prescriptions, diagnostic reports, and hospital bills, to falsely claim financial assistance. Such activities not only exploit donor trust but also divert resources away from genuinely needy patients. To address this issue, this paper proposes MediTrust, an intelligent AI-driven framework designed to detect fraudulent medical fund requests through advanced document understanding and data verification techniques. The system integrates Optical Character Recognition (OCR), Natural Language Processing (NLP), and anomaly detection models to analyze and validate uploaded documents. =The proposed approach enhances transparency and reliability in medical crowdfunding by automating the verification process and minimizing human error. Experimental observations indicate that MediTrust can effectively distinguish between genuine and suspicious cases, thereby improving trust among donors and ensuring fair distribution of funds.
KEYWORDS
MediTrust offers a comprehensive and intelligent solution to the growing problem of fraud in medical crowdfunding platforms. By combining advanced AI techniques with structured document verification processes, the system ensures that financial assistance is directed toward genuine patients. The proposed framework enhances transparency, builds donor trust, and reduces the risk of misuse. Its scalable architecture makes it suitable for large-scale deployment, while its modular design allows for future enhancements. In the future, the system can be extended by integrating real-time hospital databases, blockchain-based document verification, and more advanced deep learning models. These improvements can further strengthen the reliability and accuracy of the framework, making MediTrust a robust solution for secure and trustworthy medical fundraising.
1. S. Hussain et al., “Blockchain-Based Trust Management in Crowdfunding,” IEEE Access, 2026. 2. S. Lee et al., “Fraud Detection Using Feature Selection,” IEEE Access, 2025. 3. O. K. Alia et al., “IHSAN Crowdfunding Platform,” IEEE Access, 2024. 4. K. O. Asamoah et al., “Blockchain-Based Loan Platform,” IEEE Access, 2023. 5. I. Matloob et al., “Sequence Mining for Fraud Detection,” IEEE Access, 2022. 6. IEEE, “Standards for Artificial Intelligence and Data Verification Systems,” IEEE Standards Association, 2022. 7. R. Smith, “An Overview of the Tesseract OCR Engine,” Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), 2007. 8. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” NAACL-HLT, 2019. 9. M. Schuster and K. Nakajima, “Japanese and Korean Voice Search,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012. 10. Z. Akata et al., “CRAFT: Character Region Awareness for Text Detection,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
Publication Date
5/1/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
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