Comparative analysis of cloud computing service models for banking sector data processing requirements

Posted: Jan 12, 2017

• Victoria Flores
• Victoria Hernandez
• Zoey Brown

Abstract

The banking sector stands at a critical juncture in its digital transformation journey, with cloud computing emerging as a pivotal technology for addressing escalating data processing demands. Traditional banking infrastructure struggles to cope with the exponential growth in transaction volumes, regulatory reporting requirements, and customer analytics needs. While cloud computing offers promising solutions, the selection of appropriate service models—Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS)—remains a complex challenge for financial institutions. Existing research has largely approached cloud service model evaluation from generic perspectives, failing to account for the unique constraints and requirements of banking operations. This research addresses significant gaps in current understanding by developing a specialized evaluation framework that considers banking-specific factors such as regulatory compliance, data sovereignty, transaction security, and real-time processing capabilities. The novelty of our approach lies in the integration of computational methods from diverse disciplines, including quantum-inspired algorithms for risk assessment and bio-inspired optimization techniques for resource allocation. These methodological innovations enable a more nuanced analysis of how different cloud service models perform under the distinct workload patterns characteristic of banking operations.