Fraud Detection in Healthcare

A step-by-step guide to incorporating machine learning


Healthcare practices and operations must face life-and-death challenges every day, and for best performance organizations need to minimize waste, yet fraudsters cost healthcare providers billions of dollars a year.


Current fraud detection methods often rely on broad-strokes predictions or brute force human analysis. Make the shift to machine learning-based models with this guidebook, which includes:


  • A broader overview of the role of anomaly detection in healthcare (beyond fraud detection) and ways to integrate the process into existing workflows.

  • Code samples for a simple machine learning-based stock optimization model, along with ways to customize and improve it.

  • How to approach ROI calculations when determining the first steps towards machine learning integration.

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