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An Integrated Big Data Framework for Healthcare Financial Analytics: Fraud Detection and Cost Optimization
Subject area: Science,Engineering and Technology · Area of research: Healthcare Financial Analytics Systems
DOI: https://doi.org/10.64388/IREV9I12-1719047
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
How to cite this paper
@article{1719047,
author = {Maryann Inimfon Atakpa, Toyosi Abolaji, Nyiawung Fobellah Abetoh},
title = {An Integrated Big Data Framework for Healthcare Financial Analytics: Fraud Detection and Cost Optimization},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {12},
pages = {2282-2313},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1719047.pdf},
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},
month = {June},
doi = {https://doi.org/10.64388/IREV9I12-1719047}
}