AI-Enabled Decision Support for Architecture Design in Multi-Cloud Financial Data Platforms
Abstract
Modern financial institutions increasingly rely on multi-cloud architectures to manage large-scale, heterogeneous data and support mission-critical decision-making. Designing an efficient, secure, and scalable architecture in such environments presents significant challenges due to complex inter-cloud dependencies, regulatory compliance requirements, and the need for high availability and low latency. This paper proposes an AI-enabled decision support framework for architecture design in multi-cloud financial data platforms. The framework integrates machine learning models, optimization algorithms, and knowledge-based reasoning to recommend optimal deployment strategies, resource allocation, and data flow configurations. By analyzing historical architecture performance metrics, workload patterns, and compliance constraints, the system predicts potential bottlenecks, identifies risk areas, and suggests architecture adaptations to maximize efficiency and resilience. A case study involving a financial analytics platform demonstrates that the AI-driven recommendations improved resource utilization by 18%, reduced inter-cloud latency by 12%, and enhanced compliance adherence. The study underscores the value of AI-assisted decision support in accelerating architectural design, reducing operational risks, and improving overall system performance in complex financial data ecosystems
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