Predictive Analytics for Proactive Management of System Downtime and Security Vulnerabilities in Cloud Banking

Abstract

In the era of cloud-enabled banking, financial institutions are increasingly reliant on elastic, distributed, and multi‐tenant infrastructures which, while offering scalability and agility, also expose them to elevated risks of system downtime and security vulnerabilities. This paper proposes a comprehensive framework for leveraging predictive analytics to proactively manage and mitigate both downtime events and cyber-security weaknesses in cloud banking environments. We integrate theoretical foundations of reliability engineering, security risk modelling and machine learning-based predictive maintenance with industry practice in banking and cloud services. We present full mathematical formulations for predicting failure likelihood, mean-time-to-failure (MTTF), vulnerability exploit probability, and integrated cost‐benefit optimisation of mitigation actionsThen we provide a technical architecture for implementation in a typical cloud banking stack – including telemetry pipelines, anomaly detection, supervised/unsupervised learning, survival analysis, and reinforcement-learning for adaptive remediation. Finally we present industry application scenarios (e.g., for a large retail bank migrating to cloud) and discuss practical challenges, regulatory considerations, and future research directions. The result is a scholarly yet accessible contribution aimed at bridging the gap between advanced analytics theory and proactive operations in cloud banking.