Modern AI Powered Machine Learning Architecture for Secure Financial Systems in Cloud Ecosystems
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Abstract
The rapid integration of artificial intelligence (AI) and machine learning (ML) into financial systems, cloud infrastructures, and Internet of Things (IoT) applications has necessitated the development of next-generation architectures that ensure both performance and security. Traditional centralized architectures are increasingly inadequate in handling large-scale, real-time data streams and mitigating sophisticated cyber threats. This research proposes a novel AI and ML framework designed to enhance predictive analytics, anomaly detection, and secure transaction processing in cloud-based and IoT-integrated financial ecosystems. The proposed architecture leverages edge computing to reduce latency, blockchain-inspired mechanisms for data integrity, and federated learning models to maintain privacy while enabling collaborative insights across distributed networks. The study emphasizes the convergence of AI, ML, cloud computing, and IoT technologies to build a resilient, adaptive, and scalable financial system capable of responding to dynamic threats and complex operational demands. Simulation results demonstrate significant improvements in detection accuracy, response time, and data privacy, suggesting the architecture’s potential as a foundational model for next-generation secure financial systems. The findings highlight that integrating AI-driven intelligence with distributed computational strategies is essential for advancing financial security, operational efficiency, and user trust in a hyper-connected digital economy.
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