Banks are rapidly adopting AI to improve efficiency across fraud, risk, and third party management, but governance, accuracy, and accountability lag behind. As human oversight collides with automation, firms are redefining policies, controls, and contract
Ahead of
CeFPro's Treasury & ALM conference event in New York next month, we sat
down with Arpit Jain, Model Risk Validation Officer with NatWest Banking Group,
to discuss how banks are reassessing model risk frameworks amid market
volatility, regulatory pressure, and rapidly evolving data and technology
assumptions.
From validation
rigor to governance expectations, the conversation explores what model risk
leaders must get right as legacy models are tested by new realities.
CeFPro: We often talk about a
widening skills gap in model risk management. From your perspective, which
combinations of skills - quant, AI/ML, domain expertise, or governance - are
proving the hardest to find today?
Arpit: 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.
She told me that the future
of MRM belongs to those who are effectively bilingual, by
being both technically fluent and risk-aware.
Usually, professionals
tend to fall into one of the two categories, either highly technical and analytical
specialists, or domain experts with strong governance and judgement. It is very
rare to find holistic thinkers who can genuinely bridge
the gap between
advanced technical analysis
and robust risk management practices.
It is comparatively easy
to find strong mathematicians and statisticians, but very few of them would understand why MRM exists in
the first place.
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.
Conversely, many
professionals with deep domain expertise can readily understand validation
theory, apply it to complex models, and clearly articulate model limitations to senior management, risk committees, and regulators.
However, they often lack
the technical depth required to substantiate their theoretical understanding.
Therefore, Model risk
management increasingly requires validators who can move seamlessly between
code, controls, and committees.
Being able to challenge a
complex model based on regulations is only half the job; articulating that
challenge and supporting your claim with technical analysis is equally
critical.
This blend of analytical rigor
and professional judgement remains rare, particularly among young talent. It
typically develops only through sustained exposure and experience that cannot
be easily accelerated.
Cultivating this balance
requires time, curiosity, and humility. But it is precisely what makes this
profession both challenging and deeply rewarding.
Financial institutions are experimenting with different ways
to build cross-functional talent. What have you seen actually work in practice - whether it’s structured training, certification pathways, or
rotational programmes?
In my experience, what
works in practice is the combination of collaborative structured training
sessions and rotational programs. This builds an ecosystem of development that recognizes how model risk capabilities can be improved.
Structured training
sessions play a critical role in establishing foundational knowledge,
particularly early in a career.
Training in areas such as
statistics, econometrics, artificial intelligence, machine learning, and risk
management frameworks is essential to creating a common technical and
regulatory language, as well as a base for standard of rigor.
However, training alone
rarely produces strong model risk validators. The most impactful sessions I have seen are collaborative and problem-led, rather than syllabus-driven sessions delivered by an individual expert.
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 the development
of both young talent and experienced MRM professionals.
These rotations are most
effective when rotations are long enough for individuals to experience the real
consequences of their decisions and understanding the importance of each and
every process, rather than simply observing those
processes.
A modeler who has had to defend their work to
validation, a validator who has experienced the pressures of model delivery, or
a model user who understands the mathematical foundations behind model
construction will inevitably develop a more balanced, credible, and pragmatic
perspective on MRM.
My own career began in a
graduate program that allowed me to rotate between model development and model
validation.
Experiencing both sides of
the lifecycle not only deepened my understanding of the overall
process but also enabled
me to make a more informed
decision about where my skills were best aligned.
Ultimately, excellence in
MRM does not come from isolated
training or narrow
specialization.
It emerges from shared
problem-solving, and exposure across model lifecycle. Organizations that invest in both collaborative learning and meaningful rotations are far more
likely to build a pool of cross functional talent.
Collaboration models are changing as AI adoption grows. How
are organisations practically enabling first-line teams while still ensuring
strong second-line oversight and independence?
What I have seen work best
at NatWest is not a dilution of independence, but a clearer articulation of
roles, supported by earlier involvement and more informed engagement from the
second line.
NatWest has been embedding
model risk validators earlier in the model lifecycle, particularly during the
design and data selection stages.
This early engagement
helps first-line developers better understand regulatory expectations around
data governance, control standards, and model risk considerations.
At NatWest, this
collaboration is facilitated through regular meetings between model users and
developers, known as Model User Groups (MUGs), which are also attended by MRM
experts.
Their involvement is
explicitly advisory rather than permissive. Independence is preserved because
formal validation opinions are formed later based on documented evidence and
with clear separation of accountability.
