Unsupervised Machine Learning for Explainable Health Care Fraud Detection

46 Pages Posted: 13 Feb 2023 Last revised: 20 Jan 2025

See all articles by Shubhranshu Shekhar

Shubhranshu Shekhar

Brandeis University - International Business School

Jetson Leder-Luis

Boston University

Leman Akoglu

Carnegie Mellon University

Date Written: February 2023

Abstract

The US spends more than 4 trillion dollars per year on health care, largely conducted by private providers and reimbursed by insurers. 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 work, we develop novel machine learning tools to identify providers that overbill insurers. Using large-scale claims data from Medicare, the US federal health insurance program for elderly adults and the disabled, we identify patterns consistent with fraud or overbilling among inpatient hospitalizations. Our proposed approach for 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 case studies of suspicious providers validate our approach and findings. We also perform a post-analysis to understand hospital characteristics, those not used for detection but associate with a high suspiciousness score. Our method provides an 8-fold lift over random targeting, and can be used to guide investigations and auditing of suspicious providers for both public and private health insurance systems.

Suggested Citation

Shekhar, Shubhranshu and Leder-Luis, Jetson and Akoglu, Leman, Unsupervised Machine Learning for Explainable Health Care Fraud Detection (February 2023). NBER Working Paper No. w30946, Available at SSRN: https://ssrn.com/abstract=4356230

Shubhranshu Shekhar (Contact Author)

Brandeis University - International Business School ( email )

Mailstop 32
Waltham, MA 02454-9110
United States

Jetson Leder-Luis

Boston University ( email )

595 Commonwealth Avenue
Boston, MA 02215
United States

Leman Akoglu

Carnegie Mellon University ( email )

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