Current Volume 9
An Integrated Big Data Framework for Healthcare Financial Analytics: Fraud Detection and Cost Optimization
Abstract
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.
Keywords
Healthcare Financial Management, Big Data Analytics, Snowflake, Tableau, Fraud Detection, Cost Analytics; Value-Based Care, Readmission Prediction, NHS, UK GDPR
Citations
IRE Journals:
Maryann Inimfon Atakpa, Toyosi Abolaji, Nyiawung Fobellah Abetoh "An Integrated Big Data Framework for Healthcare Financial Analytics: Fraud Detection and Cost Optimization" Iconic Research And Engineering Journals Volume 9 Issue 12 2026 Page 2282-2313 https://doi.org/10.64388/IREV9I12-1719047
IEEE:
Maryann Inimfon Atakpa, Toyosi Abolaji, Nyiawung Fobellah Abetoh
"An Integrated Big Data Framework for Healthcare Financial Analytics: Fraud Detection and Cost Optimization" Iconic Research And Engineering Journals, 9(12) https://doi.org/10.64388/IREV9I12-1719047
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