AI Driven Banking Ecosystems: Leveraging Cloud Based BMS for Zero Downtime Upgrades
DOI:
https://doi.org/10.15662/IJRAI.2025.0806801Keywords:
Banking management system, cloud native architecture, zero downtime upgrades, artificial intelligence, banking ecosystem, operational resilienceAbstract
In the era of digital banking, financial institutions face mounting pressure to deliver continuous, resilient, and differentiated services while simultaneously upgrading their core systems without disruption. This paper explores how banking ecosystems can leverage cloud‑based Banking Management Systems (BMS) combined with artificial intelligence (AI) to achieve near zero‑downtime upgrades and agile operations. We examine the architecture, processes, and enablers of a cloud‑native BMS, and how AI capabilities (such as predictive analytics, anomaly detection, automated orchestration) integrate with the BMS to support continuous availability even during major upgrade events. A methodology is proposed to assess upgrade readiness, deploy cloud‑based BMS upgrades in a phased, automated, rollback‑capable manner, and monitor service continuity. We identify the advantages of this approach—such as operational agility, cost efficiency, enhanced customer experience, and improved risk management—as well as its disadvantages, including regulatory and security concerns, cultural change, and technical complexity. The paper discusses sample results (e.g., reduction in planned downtime, faster feature roll‑out, fewer rollback incidents) and offers practical recommendations for banking institutions embarking on this journey. Finally, future work is outlined in terms of standardising upgrade frameworks, governance models for AI‑driven operations, and ecosystem interoperability.
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