As generative AI moves from hype to deployment, large banks are extending mature model risk frameworks rather than rebuilding them. A senior model risk leader explains why governance, adaptability, and human judgment are becoming more important as institu
The surge of enthusiasm around
generative AI over the past year has been dramatic, moving quickly from
experimentation to structured implementation across financial services.
For risk leaders inside large banks,
the challenge has not been whether to engage with Gen AI, but how to govern it
responsibly without destabilizing long established control frameworks.
Speaking at a New York conference of
senior risk managers, the director of model validation at a major US bank
described the current phase as a necessary shift from excitement to discipline.
“I speak from experience when I say
we’ve moved past the initial euphoria and into a period where governance,
controls, and accountability really matter,” he told delegates.
Rather than treating generative AI as
a wholly new risk category, his team has focused on extending existing model
risk management foundations.
Years of experience validating
traditional models have provided a strong base for addressing Gen AI risks such
as bias, fairness, explainability, and toxic outputs.
The executive emphasized that
reinventing frameworks would create unnecessary fragmentation. The priority
instead has been to layer new controls onto proven structures.
Model validation sits at the center
of this effort. While governance teams own formal policies and procedures,
validators play a critical role in shaping how new risks are identified and
operationalized.
According to the speaker, this work
goes far beyond technical testing. It also involves influencing policy design,
collaborating across functions, and embedding controls that reflect how models
are actually used in business contexts.
That collaborative approach, he says,
has become more important as regulatory expectations evolve. Supervisory
guidance on artificial intelligence is still emerging, and standards differ
across jurisdictions.
The speaker noted that adaptability
is essential. “We cannot afford to be reactive,” he said. “We design our
policies knowing that regulation will change, and we need to absorb that change
smoothly.”
Large institutions have an advantage
in this environment because they can draw on expertise from legal, compliance,
technology, and risk teams simultaneously.
When new guidance appears, it can be
interpreted and integrated quickly rather than bolted on as an afterthought.
This, he argues, helps to maintain consistency across the model lifecycle and
avoids creating parallel governance tracks for Gen AI.
Industry dialogue has also reinforced
a sense of convergence. Through discussions with peers at major regulatory and
industry events, the executive told delagtes he had observed that large banks
are approaching Gen AI with similar levels of caution.
