Unsupervised Machine Learning for Explainable Health Care Fraud Detection

TL;DR

This paper introduces an unsupervised, explainable machine learning method to detect Medicare fraud by analyzing large claims data, providing interpretable insights into suspicious provider behaviors without relying on labeled training data.

Contribution

The paper presents a novel unsupervised and explainable approach for Medicare fraud detection, validated with real data and case studies, advancing privacy-preserving and interpretable healthcare fraud analytics.

Findings

Effective identification of potentially fraudulent providers

Validation with DOJ lawsuit data supports approach accuracy

Provides interpretable insights into suspicious billing patterns

Abstract

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.