"AI FOR FRAUD DETECTION, PREVENTION, AND MANAGEMENT IN HEALTHCARE SYSTEMS"
Keywords:
AI (Artificial Intelligence),Anomaly Detection,Bias,Deep,Learning,Ethics, Fraud Prevention, Machine Learning, Natural Language Processing (NLP),Reinforcement Learning, Supervised LearningAbstract
Healthcare fraud is a global issue that is escalating in scope and ranges from billions annually to threaten the financial sustainability and quality of patient care. Fraud involving false billing, identity theft, or overbilling had been a challenge for healthcare systems. Conventional methods of fraud detection, seen in the healthcare sector, no longer fulfill their purpose, mainly because of the challenge to manage the increasingly high volume and complexity of healthcare transactions. Artificial intelligence techniques, known as machine learning, natural language processing, and anomaly detection, have begun to lead this endeavor to identify and prevent such unduly activities in healthcare.
The intent of the paper is to look into the applications of AI in the domain of fraud detection. This corresponds to a general review of various methods that could be implemented for this purpose, followed by one or more case studies to illustrate these methodologies, delineate problems, and discuss ethical questions.
