Cloud-Native Infrastructure and DevOps Practices for AI-Driven Insurance Systems

Part of the appeal of a cloud-native architecture is the ability to break away from traditional hosting strategies—infrastructure-as-a-service (IaaS) models—toward more abstracted offerings. These facilities are not constrained by location but can also leverage the best characteristics of the multi-cloud paradigm, diversifying risk or better serving certain workloads. Insurance systems in general, and the high-scale, development-intensive areas (claims handling, modeling) in particular, are ideal candidates for serving in an abstracted platform-as-a-service (PaaS) environment.

Cloud-native architectures are the last step in the automation and abstraction journey, riding on the immense popularity of PaaS technologies and practices in user-defined software development. The technology and the business are ideal complements: Higher frequencies of releases lower the overall maintenance overhead; architectural pressure toward smaller granularities enables recognition of the impact of code or resource errors; and modern monitoring capability can correlate the myriad of data generated to identify incidental and stochastically significant signals to inform and improve the operational choice of the system—with limited overall cost across initial deployment, maintenance, and operational activities. Indeed, for these reasons, even unregulated industries and startups are embracing the democratization of Machine Learning (ML) capabilities.