Meditrust: An Ai-based Medical Fund Verification System For Fraud Detection And Donor Trust Enhancement

• Author(s):

  A.Karthika | K.Mohamed Hairur Raheem | N.Ahamed Irfan | R.Karthikeyan | B.Athiban

• Keywords:

  Medical Crowdfunding, Fraud Detection, Docu- Ment Verification, Deep Learning, CRAFT, Donut Model, Fuzzy Matching, Healthcare Analytics, AI-based Systems, Document Un- Derstanding.

• Abstract:

  This Document Presents A Demonstration Paper For Submissions To The SET International Journal Of Broadcast Engineering (SET IJBE). It Provides Authors With A Practi- Cal Example Of An AI-based Medical Fund Verification System, Including The Integration Of Deep Learning Models, Document Processing Techniques, And Fraud Detection Mechanisms. The Paper Illustrates The Structure Of A Complete Manuscript, Covering Title, Author Information, Abstract, Keywords, Sections, Figures, Tables, Equations, Lists, References, And Acknowledgments. The Proposed System, MediTrust, Utilizes CRAFT For Text Region Detection, Donut For Document Understanding And Structured Data Extraction, And Fuzzy Matching Algorithms For Validating Ex- Tracted Information Against A Trusted Hospital Database. The Sam- Ple Content Demonstrates How Automated Verification Improves Transparency, Reduces Manual Effort, And Detects Fraudulent Medical Fund Requests Efficiently. This Abstract Is Limited To Fewer Than 150 Words, In Accordance With The Journal Guidelines, And Is Intended To Guide Authors In Preparing Compliant, Consistent, And Publication-ready Submissions. It May Also Serve As A Reference When Adapting The Template To Real-world AI-based Research Contributions.

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Other Details

• Paper id:

  IJSARTV12I4105223

• Published in:

  Volume: 12 Issue: 4 April 2026

• Publication Date:

  2026-04-30

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