Automated Model Validation: Fact or Fiction?
There is resounding evidence that financial institutions are becoming increasingly model-driven in their decision-making and core business processes. Artificial intelligence and data analytics are no longer science experiments, but transformational board-level initiatives.
As a result, there is a massive proliferation of analytical models across the modern financial enterprise – and the number and complexity of these models will only grow exponentially. This puts an ever-increasing burden on the internal model validation teams to ensure adherence to the multitude of regulatory processes. The problem is exacerbated by the fact that machine learning models require more frequent model refreshes—and thus re-validation—to drive the desired business efficacy. Additionally, increasing desire to use black-box models for which there is no explicit regulatory guidance, thus increasing the model validation efforts.
The massive growth in model validation backlog has led to substantial YoY growth in OPEX for the model validation teams, which is particularly challenging in today’s climate where financial institutions are under tremendous pressure to dramatically reduce costs.
So the question: Is it possible to curb or even decrease model validation spend?
Over the past several years, we’ve seen financial institutions embark on efforts to attempt to automate the model validation process but struggle due to a variety of issues, such as:
Disparate model development processes across teams
Ad hoc data set creation and management during model development
Limited model and data traceability
“Tribal knowledge” locked in the heads of validators
Introduction of “new wave” of languages/frameworks (R, Python, Tensor) and model development tools (Juptyer, RStudio, AutoML) that have limited-to-no model governance standards
Right Partner With the Right Solution
Even in the face of these challenges, with the right partner and the right solution, it is possible to optimize the OPEX in the model validation process through an automated validation solution. The results are substantial!
We don’t aim to automate all of your processes or solve all of your Model Risk Management challenges. But think about your annual OPEX savings by automating just 30% of your model validation workload.
Decreased costs by 20-30%
Increased risk transparency across the entire modeling process
Support Data Science
Increased agility to support data science