Losses due to Medicare fraud in the United States are estimated to inflate public health expenditures from between 3 and 10%. This means that up to $300 billion is misappropriated from Medicare patients into the hands of criminals on an annual basis. Despite extensive efforts from state and federal law enforcement, only a fraction of Medicare fraud is thought to be identified and further investigated to lead to convictions. The difficulty in identifying health care fraud is due to the range of deceptive methods used and the difficulty to distinguish legitimate from fraudulent claims. This has lead to a lucrative market for nefarious parties and has garnered the participation of international rings of criminals to partake.

Machine learning is an important tool that is becoming increasingly utilized to identify providers involved in fraudulent transactions. This project uses a mix of unsupervised and supervised machine learning models for the identification of fraudulent inpatient providers and is a step forward towards stopping swindlers.

The data used for this project are from Medicare claims from 2009 and is available from Kaggle

• Python 3.0 and above
• Jupyter notebooks
• Numpy
• Pandas
• Seaborn
• Scikit-learn