The DYNAMIC FINANCIAL PLANNING WITH REINFORCEMENT LEARNING IN CLOUD BI: OPTIMIZING RISK MANAGEMENT, ACCOUNTING, AND BUDGETING USING OPEN CIRRUS

DYNAMIC FINANCIAL PLANNING WITH REINFORCEMENT LEARNING IN CLOUD BI

Abstract

Background Information: The integration of Reinforcement Learning (RL) with Cloud Business Intelligence (BI) has revolutionized dynamic financial planning by enhancing the accuracy and scalability of decision-making processes. The Open Cirrus platform enables real-time data processing, ensuring companies can respond to financial risks, optimize budgeting, and improve resource allocation efficiently.

Objectives: This paper aims to develop a framework that incorporates RL in Cloud BI for optimizing financial planning. Key objectives include improving risk management, enhancing decision-making, and utilizing Open Cirrus to achieve scalability, adaptability, and efficient resource allocation in budgeting and financial risk assessment.

Methods: The methodology integrates RL to learn financial strategies based on historical data. Cloud BI facilitates real-time insights, while Open Cirrus handles large-scale data processing. The RL model was trained to dynamically adjust resource allocations and optimize financial plans, based on performance metrics like accuracy, latency, and scalability.

Results: The proposed framework significantly improved financial accuracy (93.5%), minimized error rates (0.034), and enhanced scalability (97.6%) while maintaining low latency (120 ms) in real-time financial planning and risk management.

Conclusion: The integration of RL, Cloud BI, and Open Cirrus delivers an effective solution for dynamic financial planning. The framework enhances financial decision-making, risk management, and budgeting by improving accuracy, scalability, and adaptability in real-time, ensuring sustainable business growth and optimized resource allocation.