AI-DRIVEN HEALTHCARE PAYMENT SYSTEMS USING INTELLIGENT CLAIMS VALIDATION AND FRAUD DETECTION MECHANISMS (International Journal of Engineering & Extended Technologies Research (IJEETR), 2024)
The rapid evolution of digital healthcare ecosystems has significantly transformed the way medical services are delivered and financed. However, healthcare payment systems continue to face persistent challenges such as inefficient claims processing, billing inaccuracies, delayed reimbursements, and increasing instances of fraud and abuse. Traditional rule-based systems often lack the scalability and adaptability required to address the growing complexity of modern healthcare transactions.This paper presents a comprehensive overview of AI-driven healthcare payment systems that leverage advanced machine learning and intelligent automation to enhance claims validation and fraud detection mechanisms. By integrating techniques such as predictive analytics, anomaly detection, natural language processing, and pattern recognition, these systems enable real-time verification of claims, identification of suspicious activities, and optimization of reimbursement workflows.The proposed approach emphasizes the use of data-driven models to improve accuracy, reduce manual intervention, and ensure compliance with regulatory standards. Additionally, the study explores architectural considerations, including cloud-based deployment, interoperability with electronic health records (EHR), and secure data exchange frameworks.Through analytical discussion and conceptual modeling, this paper demonstrates how AI-enabled payment systems can significantly reduce financial losses, enhance operational efficiency, and improve trust among stakeholders, including providers, payers, and patients. The findings highlight the transformative potential of artificial intelligence in building resilient, transparent, and scalable healthcare financial infrastructures.
Keywords: Artificial Intelligence (AI); Healthcare Payment Systems; Claims Validation; Fraud Detection; Machine Learning; Predictive Analytics; Anomaly Detection; Healthcare Fraud Prevention; Intelligent Automation; Electronic Health Records (EHR); Data Security and Compliance; Digital Health Ecosystems