Fraud detection in healthcare claims using machine learning: A systematic review.

Journal: Artificial intelligence in medicine
PMID: 39756221

Abstract

OBJECTIVE: Identifying fraud in healthcare programs is crucial, as an estimated 3%-10% of the total healthcare expenditures are lost to fraudulent activities. This study presents a systematic literature review of machine learning techniques applied to fraud detection in health insurance claims. We aim to analyze the data and methodologies documented in the literature over the past two decades, providing insights into research challenges and opportunities.

Authors

  • Anli du Preez
    Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, United States of America. Electronic address: anlidupreez@vt.edu.
  • Sanmitra Bhattacharya
    AI Center of Excellence, Deloitte & Touche LLP, New York, NY, USA. sanmbhattacharya@deloitte.com.
  • Peter Beling
    Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, United States of America. Electronic address: beling@vt.edu.
  • Edward Bowen
    AI Center of Excellence, Deloitte & Touche LLP, New York, NY, USA.

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