Implemented an Azure-native AI solution to detect fraudulent reimbursement claims by identifying duplicate and manipulated medical images, improving accuracy, efficiency, and trust in healthcare operations.
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The challenge
The organisation faced increasing levels of fraud in reimbursement claims, particularly through the submission of duplicate or modified radiological images. With high volumes of claims and manual data entry processes, it was difficult to accurately detect similarities across documents. This resulted in financial losses, operational inefficiencies, and limited ability to scale fraud detection efforts effectively.
The approach
Designed and developed an AI-driven solution leveraging Microsoft Azure to analyse and compare medical images for similarity detection. The platform used advanced image processing and machine learning techniques to identify duplicate and altered images, including cropping, colour changes, and contrast adjustments. A centralised system was implemented to process large volumes of claims data, supported by scalable Azure infrastructure, enabling automated detection and streamlined workflows for fraud analysis.
The outcome
Delivered a highly effective fraud detection platform capable of identifying duplicate and manipulated medical images with high accuracy. The solution significantly reduced fraudulent claims, improved operational efficiency, and strengthened trust across stakeholders. With a scalable architecture, the platform supports increasing data volumes and evolving fraud patterns, providing a robust foundation for ongoing innovation in healthcare fraud prevention.
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