Serverless Cloud Computing for Efficient Retirement Benefit Calculations
Akshay Sharma, Satish Kabade · INTERNATIONAL JOURNAL OF CURRENT SCIENCE · 2025
With the rise of virtualization technologies and the rapid advancement of cloud computing, serverless architectures have emerged as a transformative paradigm for building scalable, cost-efficient, and event-driven applications without the complexities of managing underlying infrastructure. This paper presents an in-depth exploration of serverless cloud computing and its potential to modernize retirement benefit calculations by offering an adaptable and resource-efficient alternative to traditional computing models. Beginning with a review of the evolution of cloud service models—Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS)—the study delves into the fundamental differences between Function as a Service (FaaS) and Backend as a Service (BaaS), which together underpin the core of serverless computing. It provides a comparative analysis of major serverless platforms, including AWS Lambda, Azure Functions, and Google Cloud Functions, examining factors such as scalability, language support, pricing models, and integration capabilities. Building on this technical foundation, the paper investigates how serverless computing can support real-time benefit projections, dynamic annuity modeling, and automated data processing within pension systems by seamlessly integrating with payroll systems, actuarial databases, and regulatory frameworks. It further proposes a serverless framework that incorporates AI-powered analytics to enable intelligent fund allocation, personalized retirement planning, and continuous compliance monitoring.