The GRADIENT BOOSTING MACHINES-ENHANCED CLOUD BUSINESS INTELLIGENCE FOR FINANCIAL RISK MANAGEMENT, ACCOUNTING SIMULATIONS, AND BUDGET OPTIMIZATION WITH GREENCLOUD

FINANCIAL RISK MANAGEMENT, ACCOUNTING SIMULATIONS, AND BUDGET

Abstract

Background Information: The integration of Gradient Boosting Machines (GBMs) with Cloud Business Intelligence (BI) has revolutionized financial risk management, accounting simulations, and budget optimization. Traditional financial processes are time-consuming and error-prone, making automated systems essential for accurate predictions, real-time decision-making, and optimized resource allocation, particularly in complex financial environments.

Objectives: The study aims to enhance financial risk management by improving the accuracy of risk assessments, optimize budget allocations, and improve real-time financial simulations using GBMs and Cloud BI. Additionally, it seeks to reduce the carbon footprint through the use of GreenCloud energy-efficient infrastructure.

Methods: GBMs are integrated with Cloud BI for predictive modeling, while GreenCloud ensures energy-efficient processing of large datasets. The methodology focuses on real-time data analysis, budget forecasting, and financial scenario modeling. The system is evaluated using performance metrics such as accuracy, precision, recall, and latency.

Results: The proposed system demonstrates 92.4% accuracy, 0.032 MSE, and 98.3% scalability. It offers superior financial outcomes, accurate risk predictions, and optimized budget allocations while reducing energy consumption in cloud computing environments.

Conclusion: GBM-enhanced Cloud BI, coupled with Green Cloud, significantly improves financial risk management, budget optimization, and accounting simulations. The approach not only enhances decision-making processes but also aligns with sustainability goals, ensuring efficient and eco-friendly financial operations for organizations.