  The US federal government spends more than a trillion dollars per year on
health care, largely provided by private third parties and reimbursed by the
government. A major concern in this system is overbilling, waste and fraud by
providers, who face incentives to misreport on their claims in order to receive
higher payments. In this paper, we develop novel machine learning tools to
identify providers that overbill Medicare, the US federal health insurance
program for elderly adults and the disabled. Using large-scale Medicare claims
data, we identify patterns consistent with fraud or overbilling among inpatient
hospitalizations. Our proposed approach for Medicare fraud detection is fully
unsupervised, not relying on any labeled training data, and is explainable to
end users, providing reasoning and interpretable insights into the potentially
suspicious behavior of the flagged providers. Data from the Department of
Justice on providers facing anti-fraud lawsuits and several case studies
validate our approach and findings both quantitatively and qualitatively.
