Automating Model Validation at a Tier 1 Bank

Independent model validation is a critical component of model risk management in today’s highly regulated and rapidly evolving financial environment. Poorly developed and designed models undermine strategic decision-making and reporting, eventually leading to financial losses and operational inefficiencies. The introduction of new supervisory guidance and regulations has significantly raised the bar for model validation and governance. However, not all financial firms are equipped to internally support robust model validation efforts.

In this case study, we look at how a London-based G-SIB implemented Yields for Performance to streamline its model validation process and achieve a considerable increase in productivity around model validation.


Automation has become an urgent priority to improve effectiveness, efficiency and free up model validation capacity.


The variety and complexity of models is growing, while there is no dedicated technology to assist collaborative working and report generation.


Diverse data storage and development practices require a modular and flexible tech stack that be can be seamlessly integrated with the existing core enterprise applications.

The Challenges

The bank was looking to improve its model validation function to enable greater productivity and efficiency. This vision was needed to safely meet the increasing demand for model validation and reporting and to comply with heightened regulatory scrutiny. To materialize this vision, new technology was required to automate and standardize model validation activities, while improving data and information sourcing.

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The Solution

The G-SIB chose an integrated on-premise implementation of Yields for Performance to support improvements in the standard of model validations.


Yields for Performance encourages collaboration within and across functional areas by sharing specifications, best practices and developed tools & libraries.


All validations and specifications are fully recorded for audit purposes, together with full details of users and changes.


All environments are fully managed in the Yields for Performance Suite, removing the need to manage individual Python environments, including current libraries.


Through improving the efficiency, reliability and speed of repeatable tasks and tests, the validation team reported significant increases in productivity around model validation.

The Yields Advantage

Yields provides an end-to-end Model Risk Management suite to automate MRM activities and reduce model risk. The platform consists of two technologies: Yields for Performance & Yields for Governance.

Yields for Governance is a customizable model inventory and workflow management tool that streamlines the execution of end-to-end Model Risk Management processes.

Yields for Performance is a modular data science platform that automates all quantitative model testing and documentation.

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