Cloud-Native AI and ML Solutions for Financial Risk Optimization in Healthcare ERP Environments
DOI:
https://doi.org/10.15680/IJCTECE.2022.0503005Keywords:
Cloud-native AI, Machine learning, Financial risk optimization, Healthcare ERP, Predictive analytics, Real-time decision support, Data interoperabilityAbstract
The increasing complexity of healthcare operations, coupled with stringent regulatory requirements and volatile financial environments, presents significant challenges for effective financial risk management within enterprise resource planning (ERP) systems. The integration of cloud computing, artificial intelligence (AI), and machine learning (ML) offers a transformative solution by enabling predictive, data-driven decision-making and real-time operational insights. This paper proposes a cloud-native AI and ML framework specifically designed to optimize financial risk management in healthcare ERP environments, addressing both high-volume transactional data and heterogeneous clinical datasets.
The architecture combines supervised and unsupervised machine learning algorithms to perform predictive risk assessment, anomaly detection, and scenario-based forecasting. Cloud-native deployment ensures scalable, secure, and high-performance processing of large-scale healthcare datasets, while advanced data pipelines facilitate real-time analytics and proactive financial resource allocation. Integration with ERP modules supports data interoperability, auditability, and compliance monitoring, allowing seamless alignment with organizational governance and regulatory standards. The framework also incorporates a decision support layer, providing actionable insights for financial managers, risk officers, and operational planners. Experimental evaluation, conducted on synthetic and real-world healthcare datasets, demonstrates that the system improves risk prediction accuracy by over 20%, reduces decision latency, and enhances overall financial performance compared to traditional ERP analytics approaches. By combining cloud scalability, AI/ML intelligence, and ERP integration, this framework provides a robust, adaptive, and explainable solution for financial risk optimization in healthcare organizations. The study highlights the potential of cloud-native AI and ML technologies to transform financial governance, improve operational efficiency, and support strategic decision-making in complex healthcare ERP ecosystems.
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