A Unified Data Strategy and Architecture for Financial Mastery: AI, Cloud, and Business Intelligence in Healthcare

Abstract

This architecture presented is a comprehensive, long-term proposal on how financial mastery can be empowered in healthcare using advanced data strategies, artificial intelligence (AI), and cloud-native analytics. The presented solution proposes a hybrid architecture of AI-powered data strategy modeling, cloud-based financial pipelines, and independent layers of data integration in order to allow real-time, high-precise financial intelligence. The fundamental element of this structure is the Dynamic Adaptive Data Architecture (DADA), created to harmoniously combine the heterogeneous financial, clinical and operational data in a secure, scalable and interoperable cloud system. The architecture is also based on an AI-enhanced Business Intelligence (AI-BI) Engine, which uses machine learning, anomaly detection, and predictive analytics in budgeting, cost optimization, fraud detection, financial risk prediction, and forecasting. 

To enhance this, a new Cognitive Financial Integration Model (CFIM) is presented to automate the data quality management process, semantic harmonization, and cloud-based ETL/ELT processes. The framework was tested with real-life healthcare finance data on claims, revenue cycle and operational expenditure data. It has been proven that the accuracy of monthly ledgers was increased by 7.5 percent (91.2 percent to 98.7 percent), the cases of unresolved variances were decreased by 1,000 to 169 cases (-83.1 percent), and the time of error-detection was shortened by 9.6 days to 1.8 days (-81.3 percent), which are all result of a demonstrated increase of the accuracy of the monthly ledgers through automated reconciliation. The predictive reconciliation also improved the optimization of financial adjustments as there was a reduction of 56.2 claims underpayment, 55.1 write-offs, and 67.7 accrual mismatch. The efficiency at the end of period was significantly improved with a reduction of the month-end close cycle time by 58.3 per cent, reduction of the time spent on manual reconciliation by 71.7 per cent, and reduction of the number of audit adjustments per quarter by 73.5 per cent. These findings prove that the suggested AI-cloud financial architecture creates a strong basis of smart, open, predictive, and audit-on healthcare financial ecosystems.