SCALABLE SAS-BASED ARCHITECTURE FOR ENTERPRISE CREDIT RISK DATA PROCESSING IN CLOUD ENVIRONMENTS

Abstract

The growing volume and complexity of financial data have created significant challenges for enterprise credit risk management systems. Traditional on-premises infrastructures often struggle to process large-scale credit risk datasets efficiently, resulting in increased operational costs and limited scalability. This study proposes a scalable SAS-based architecture for enterprise credit risk data processing in cloud environments. The proposed framework integrates SAS analytical capabilities with cloud-native computing resources to support efficient data ingestion, transformation, risk modeling, and reporting processes. A hypothetical experimental methodology was employed to evaluate the architecture using enterprise-scale credit risk datasets under varying workload conditions. The results demonstrated substantial improvements in processing performance, throughput, resource utilization, and system scalability. The architecture effectively supported concurrent processing jobs while maintaining high system availability and operational reliability. Furthermore, the implementation of security controls and compliance mechanisms ensured secure handling of sensitive financial data. The findings indicate that cloud-based SAS architectures provide a flexible, scalable, and cost-effective solution for modern credit risk analytics and portfolio management.