An Integrated Big Data Framework for Healthcare Financial Analytics: Fraud Detection and Cost Optimization
Maryann Inimfon Atakpa, Toyosi Abolaji, Nyiawung Fobellah Abetoh · Iconic Research and Engineering Journals · 2026
Healthcare organisations face mounting financial management complexity driven by rising treatment costs, expanding regulatory compliance requirements, and the transition toward value-based payment models. This paper proposes an integrated big data analytics framework for healthcare financial management comprising four analytically distinct but technically integrated modules: a claims integrity module using ensemble machine learning for fraud, waste, and abuse detection; a cost driver analytics module using Snowflake and Tableau for cost centre performance monitoring; a value-based care analytics module for quality measure reporting and provider performance benchmarking; and a predictive cost modelling module using XGBoost regression for 90-day readmission cost forecasting. The framework addresses NHS-specific implementation requirements including UK GDPR compliance, NHS information governance constraints, diversity of NHS payment models, and organisational capability requirements. A phased NHS implementation roadmap and framework architecture table are presented.