Cloud-Native Risk Modeling: Enhancing Flexibility Through Transition

About the Client

A leading regional bank in the United States serves a diverse customer base, including retail, small business, corporate, commercial, and investment clients. With a strong national presence, it operates hundreds of branches and thousands of ATMs across multiple states.

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The Challenge

The bank relied on a SAS ECL-based solution to calculate risk metrics, including Probability of Default, Loss Given Default, and Exposure at Default, across various economic scenarios and loan portfolios. However, they sought to migrate to a more flexible, Python-based system while minimizing the risk of user code changes.

The Solution

Designed and implemented a front-end UI/UX for orchestrating Cloud-Native Risk Modeling, ensuring a seamless transition to the new system. The institution provided the code and machine learning model suites, while the integration and orchestration of the models were handled within the Google Cloud Platform (GCP) ecosystem. The solution maintained functionality parity with the SAS ECL platform while leveraging the flexibility and scalability of Python-based workflows.

The Result

Risk Minimization: Transitioned to a modern platform with minimal disruptions or code changes.

Enhanced Flexibility: Enabled efficient orchestration and scalability of Cloud-Native Risk Modeling using Python.

User-Centric Design: Delivered a robust UI/UX for seamless interaction with the new system.

Future-Ready Infrastructure: Leveraged GCP to provide a scalable foundation for ongoing risk analysis.