Abstract :

Healthcare Fraud Is A Major Issue In The Medical And Insurance Industry, Leading To Significant Financial Losses And Reduced Trust In Healthcare Systems. Fraudulent Claims Include False Billing, Unnecessary Treatments, And Misuse Of Insurance Services. Traditional Fraud Detection Methods Rely On Manual Verification And Classical Machine Learning Techniques, Which Often Suffer From Limited Accuracy, High Processing Time, And Difficulty In Handling Complex Datasets. With The Advancement Of Quantum Computing, Quantum Machine Learning (QML) Has Emerged As A Powerful Solution That Utilizes Quantum Properties Such As Superposition And Entanglement To Improve Computational Efficiency. This Project Focuses On Developing A Healthcare Claim Fraud Detection System Using Quantum Techniques Like Quantum Support Vector Machine (QSVM) And Variational Quantum Classifier (VQC). The System Analyzes Patient And Claim-related Data Such As Age, Medical History, Billing Details, And Treatment Records To Identify Fraudulent Patterns. By Encoding Classical Data Into Quantum States And Processing It Through Quantum Circuits, The Model Improves Pattern Recognition And Classification Accuracy. The Proposed System Integrates Classical Preprocessing With Quantum Algorithms To Achieve Better Performance Compared To Traditional Models, And Experimental Results Show Higher Accuracy, Faster Processing, And Improved Efficiency, Demonstrating The Potential Of Quantum Computing In Healthcare Fraud Detection. In Addition, The System Reduces Dependency On Manual Verification And Increases Automation In The Fraud Detection Process. It Also Helps In Identifying Hidden Patterns That Are Difficult To Detect Using Traditional Systems. The System Contributes To Improving Decision-making Processes In Insurance Companies. Overall, The Proposed Model Provides A Reliable And Efficient Solution For Modern Healthcare Challenges.

Published:

01-5-2026

Issue:

Vol. 26 No. 5 (2026)

Page Nos:

48-51

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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