AI-Assisted Financial Governance Models for Multi-Cloud Cost Optimization

Abstract

Multi-cloud environments increase cost as they have price variations, poor monitoring, and lack cost accountability across these environments. Also, these cost management tools have poor visibility into dynamic application patterns, inter-cloud dependencies, and future financial risk assessment. Cloud cost management tools usually rely on static budgets and rule-based alerts and reports-based approaches. In a multi-cloud financial management environment enabled through the help of Artificial Intelligence, better cost efficiency is achieved through enriched usage analytics and forecasts, and policy-based automation. Here, context-aware financial management, flexible budgeting, and intelligent anomaly detection offer better financial governance and decision-making capabilities. Also, federated analytics and reinforcement learning provide better trade-offs in model accuracy, cost, and compliance with transparency. Also, scalable financial governance, predictive cost optimization, and financial responsibility provide key concepts in such a financial management system. This enables better corporate sustainability, autonomous financial operations, and integrated cross-cloud financial governance.