As artificial intelligence reshapes financial modeling, model risk management is evolving beyond traditional validation. Arpit Jain of NatWest Group explains why new skills, stronger collaboration across lines of defense, and deeper lifecycle oversight ar
Model risk management is undergoing a
profound transformation as artificial intelligence and advanced analytics
reshape how financial institutions build and deploy models.
What was once primarily a technical
validation exercise has expanded into a broader governance challenge that
requires new skills, stronger collaboration, and deeper oversight across the
model lifecycle.
According to Arpit Jain, Model Risk
Validation Officer at NatWest Group, the biggest challenge facing the
discipline is not simply technological change but the widening gap between
technical expertise and risk awareness.
“I can best answer this by sharing a
snippet from a conversation I had with a senior manager in MRM at the beginning
of my career,” he explains. “She told me that the future of MRM belongs to
those who are effectively bilingual, by being both technically fluent and
risk-aware.”
In practice, however, such
professionals remain rare. Jain notes that many candidates fall into one of two
categories.
Some are highly technical specialists
with strong quantitative or machine learning backgrounds, while others bring
deep domain knowledge and governance experience. Very few combine both
perspectives.
“It is comparatively easy to find
strong mathematicians and statisticians,” he says. “But very few of them would
understand why MRM exists in the first place.”
At the same time, professionals who
excel in governance and risk frameworks often lack the technical depth required
to interrogate increasingly complex models.
This imbalance creates a significant
challenge for banks attempting to strengthen oversight of modern modeling
approaches.
“Many candidates are proficient in
AI, machine learning techniques, open source tools, and experimental modelling
approaches, yet struggle to translate these capabilities into credit risk,
market risk, or financial crime contexts in a way that aligns with regulatory
expectations,” Jain explains.
For model risk management to remain
effective, validators must be able to operate across technical and governance
domains simultaneously.
“Model risk management increasingly
requires validators who can move seamlessly between code, controls, and
committees,” he says.
Developing that combination of skills
requires deliberate investment. Jain believes the most effective institutions
combine structured training with cross-functional career development.
“In my experience, what works in
practice is the combination of collaborative structured training sessions and
rotational programmes,” he says.
Traditional training helps establish
the technical and regulatory foundations needed in the discipline. However,
Jain argues that the most impactful learning happens through collaboration on
real problems.
“The most impactful sessions I have
seen are collaborative and problem led, rather than syllabus driven,” he
explains. “When people come together to discuss real models, real data
limitations, interpretation of governance standards, and practical validation
challenges, it fosters a learning environment that supports development.”
