Automated Model Validation

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.