Aug 2026· International Journal of Research Publication and Reviews· 0 citations
TL;DR
Integrated AI–Health FinTech systems can strengthen insurance integrity while improving payment efficiency and patient affordability by examining how reduced processing costs, fewer erroneous denials, faster reimbursements, and improved payment transparency can decrease financial friction for patients and providers.
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.
Healthcare financing remains constrained by fragmented billing infrastructures, delayed claims adjudication, unpredictable out-of-pocket liabilities, inefficient payment reconciliation, and financing models that often identify patient affordability problems only after financial obligations arise. This study develops an...
Bridget Akanbi· International Journal of Res...· 0 citations
Health insurance claim processing is difficult for both policyholders and administrative teams because a claim
outcome depends on many interacting factors, such as policy validity, coverage limits, waiting periods, pre-existing conditions,
clinical-code compatibility, documentation completeness, hospital-network status...
K. Nithin, Banam Varshini Priyamvada· International Journal for Re...· 0 citations
Healthcare insurance fraud has emerged as a significant challenge due to the rapid growth of digital healthcare systems, increasing volumes of electronic insurance claims, and the evolving complexity of fraudulent activities. Conventional fraud detection approaches primarily rely on manually defined rules, single-modal...
Nasir Nazir, Roma Fayaz, Sharyar Wani· International journal of com...· 0 citations
Overall, the findings demonstrate that a compact, explainable, and statistically validated machine learning framework can achieve strong, stable, and computationally efficient fraud discrimination, providing a methodologically sound foundation for extending claims-based fraud analytics into real-time, EVV-integrated Me...
Md Sibbir Hossain, A. T. M. Fokrule Hasan· Frontiers in Computer Scienc...· 0 citations
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