CLOUD-NATIVE RISK ANALYTICS AT SCALE: KUBERNETES-BASED DISTRIBUTED SYSTEMS FOR ACCELERATING CREDITRISK MODELING IN FINANCIAL INSTITUTIONS

Abstract

Standard credit risk modeling infrastructures face serious limitations in serving modern needs for near real-time decisioning, regulatory flexibility, and computational scale. This case details the transformation of a multinational financial institution'scredit risk analytics infrastructure by introducing Kubernetes-based distributed infrastructure. The containerized architecture deploys GPU-accelerated compute nodes, service meshes for secure communications, and observability frameworks for monitoring thereliability of the system.Results from the implementation demonstrated remarkable reductions in Monte Carlo simulation run times,machine learning model training times, and regulatory reporting cycles, while increasing overall system uptime and deployment speed. In addition to the technical performance improvements, the transformation created compliance-by-design in the system through embedded governance controls and alignment across organizational roles of data scientists, engineers, and compliance officers. Ongoing challenges faced in the transformation include the cost to operate in the cloud, governance of data in a jurisdiction, and accommodating the workforce for acceptance of the containerized environment. Overall, the case demonstrates that cloud-native architectures could serve as a strategic enabler to operational resilience and regulatory competitiveness, with many insights into the modernizing infrastructure that financial institutions are faced with from a perspective of compliance.