Applied machine learning for health insurance fraud detection

Research and develop a machine learning application to detect fraud in health insurance claims. The project will seek to understand how machine learning can contribute significantly to health insurance fraud detection, and develop a methodology to yield the best results using available data and current machine learning best practices. The output of the project will be a unique machine learning framework to enable health insurance fraud detection, an engine that can be integrated with existing insurance claim software and a front-end dashboard for analysts. The contribution of this research will be to provide a new, automated approach to insurance fraud detection, saving significant time and money to the public and private sector alike.

Faculty Supervisor:

Vladimir Makarenkov


Nadia Tahiri



Computer science


Information and communications technologies




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