Artificial Intelligence-Driven Health FinTech Systems for Improving Healthcare Financing, Payment Efficiency, and Patient Financial Accessibility

Abstract

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 artificial intelligence-driven Health FinTech framework for restructuring these processes around predictive financial intelligence and automated payment coordination. The framework integrates transaction histories, insurance eligibility, claims records, service-cost information, and appropriate affordability indicators to support pre-service cost estimation, payment-risk prediction, automated coverage verification, claims anomaly detection, and individualized payment scheduling. Rather than treating payment efficiency and financial accessibility as separate objectives, the study conceptualizes them as interconnected outcomes of an integrated financing pathway. Machine-learning models identify potential affordability constraints before treatment, while intelligent payment orchestration directs patients toward applicable insurance benefits, installment arrangements, financial assistance, or alternative financing mechanisms. Automated claims validation and reconciliation simultaneously reduce administrative delays affecting provider reimbursement. The framework incorporates explainability, human review, data minimization, and algorithmic fairness to prevent predictive financing from producing discriminatory exclusion. This approach positions Health FinTech as financial infrastructure connecting affordability prediction, payment execution, reimbursement efficiency, and patient access.