The role of Artificial Intelligence in reducing financial fraud in U.S. healthcare systems
TL;DR
Results demonstrate the potential of machine learning, combined with NLP and predictive analytics, to add value in terms of detection accuracy, false-positive rate reduction, and near real-time fraud prevention.
Abstract
Healthcare fraud is a major problem in the United States, which annually totals tens of billions of dollars and impacts financial sustainability, as well as patient confidence. Traditional forms of detection, such as manual risk audits and static category rule-based solutions, are becoming less effective in the face of fraudster sophistication and ever-evolving fraudulent tactics. Based on a systematic review of peer-reviewed articles, policy reports, and industry white papers, this article examines how artificial intelligence (AI) can be used to reduce financial fraud in U.S. healthcare systems. Results demonstrate the potential of machine learning, combined with NLP and predictive analytics, to add value in terms of detection accuracy, false-positive rate reduction, and near real-time fraud prevention. Moreover, integration with AI human-in-the-loop is demonstrated to increase efficiency with retention of supervision. However, some obstacles persist around data quality, bias of algorithms, interpretability, and regulatory aspects. In all, AI has strong potential as a game-changer in the fight against healthcare fraud if it is rolled out subject to robust governance and ethical considerations.