Integrating Artificial Intelligence and Health FinTech to Enhance Insurance Claims Processing, Fraud Detection, and Healthcare Affordability

Abstract

Health insurance systems face persistent financial and operational pressures from complex claims workflows, billing irregularities, fraudulent transactions, reimbursement delays, and increasing out-of-pocket costs that can undermine healthcare affordability. This study examines the integration of artificial intelligence and Health FinTech as a unified framework for improving claims processing, detecting fraud, and translating administrative efficiency into greater financial accessibility. The framework combines claims histories, billing records, insurance coverage, payment transactions, and provider information to support automated claim classification, eligibility verification, anomaly detection, and reimbursement reconciliation. Machine-learning models identify unusual billing patterns, duplicate claims, inconsistent service combinations, and high-risk transactions for targeted investigation rather than indiscriminate rejection. Natural language processing further enables extraction and validation of information from claims documentation, reducing manual processing requirements. Importantly, the framework connects operational improvements to affordability by examining how reduced processing costs, fewer erroneous denials, faster reimbursements, and improved payment transparency can decrease financial friction for patients and providers. Explainable fraud alerts, human review, data protection, and algorithmic fairness are incorporated to limit inappropriate claim denial and discriminatory outcomes. Integrated AI–Health FinTech systems can therefore strengthen insurance integrity while improving payment efficiency and patient affordability.