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Using Interpretable Machine Learning Methods: An Application to Health Insurance Fraud Detection

This report outlines a framework for interpretable machine learning algorithms for detecting fraud in health insurance. It discusses the challenges of using black-box machine learning models in ...
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Description: This report outlines a framework for interpretable machine learning algorithms for detecting fraud in health insurance. It discusses the challenges of using black-box machine learning models in the insurance industry and proposes various interpretable methods to enhance transparency and understanding.
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- Date: Jan 2024
- Competency: External Forces & Industry Knowledge; Strategic Insight and Integration
- Topics: Health & Disability; Health & Disability>Health insurance; Technology & Applications; Technology & Applications>Artificial intelligence & machine learning

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Using Interpretable Machine Learning Methods: An Application to Health Insurance Fraud Detection