By Digital Futures
Modernising Credit Risk Modelling for a UK Bank
Client Context
A UK retail bank sought to modernise its credit risk function to improve performance, scalability, and regulatory compliance. The ambition was to migrate from legacy SAS-based models to a cloud-enabled infrastructure capable of supporting advanced analytics and future AI adoption.
The Challenge
The bank faced a critical transition. Its credit risk models were built on legacy SAS infrastructure that limited agility, slowed innovation, and created rising costs. To remain competitive and compliant, the bank needed to shift to Python-based modelling and migrate its entire risk modelling infrastructure to Google Cloud — without disrupting core operations.
Our Approach
Digital Futures partnered with the bank to design and deliver the migration programme. We:
- Rebuilt models in Python, ensuring accuracy, scalability, and compatibility with cloud-based workflows.
- Redesigned infrastructure on Google Cloud, enabling higher performance, lower cost, and stronger integration with the bank’s wider data strategy.
- Provided specialist talent — data engineers, analysts, and risk modelling experts — to accelerate the migration and embed best practice.
- Established governance frameworks to ensure regulatory compliance throughout the transition.
The Impact
The new credit risk function delivered clear results:
- Migration from SAS to Python improved model flexibility and reduced licensing costs.
- Deployment on Google Cloud enhanced scalability and processing speed, enabling faster and more accurate risk assessment.
- Embedded governance ensured compliance and preserved confidence with regulators and stakeholders.
Outcome
The bank now operates a modern, cloud-based credit risk function that is faster, more efficient, and better aligned to its long-term technology strategy. The programme not only reduced costs but also positioned the institution to leverage advanced analytics and AI in future credit decisioning.