Hybrid AI and Apache Cloud Framework for Financial Performance Optimization in SAP-Integrated BMS
DOI:
https://doi.org/10.15662/IJARCST.2024.0706011Keywords:
Artificial Intelligence, Apache Cloud, SAP, Business Management System, Financial Performance, Predictive Analytics, Distributed Computing, Data OptimizationAbstract
In the rapidly evolving digital enterprise landscape, optimizing financial performance through intelligent automation has become essential for sustainable growth. This paper introduces a Hybrid AI and Apache Cloud Framework designed to enhance financial performance analysis and optimization within SAP-integrated Business Management Systems (BMS). The proposed model leverages Apache-based cloud architecture to manage large-scale financial data efficiently while integrating Artificial Intelligence (AI) for predictive analytics, anomaly detection, and performance forecasting. Through real-time data synchronization between SAP modules and the BMS, the system improves decision-making accuracy and operational transparency. AI algorithms evaluate key performance indicators (KPIs) to identify trends, reduce financial risks, and ensure compliance with corporate governance standards. Apache’s distributed computing capabilities further enable scalability, fault tolerance, and high-speed data processing across hybrid cloud environments. Experimental validation demonstrates significant improvements in financial data accuracy, process efficiency, and overall business intelligence. The framework establishes a foundation for intelligent financial ecosystems combining AI, Apache, and SAP technologies.
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