Much of AI’s potential has yet to be imagined. But already
we are seeing how it can: increase efficiency and productivity,
enhance customer and employee experience, improve analytics and aid
innovation.

However, it’s no secret that AI has potential to be
maliciously used to disrupt business.

Assem Marat, Senior FI Underwriter at Starr Companies joins
Trenton McNee, FinTech and Digital Assets Industry Leader, FINEX
Financial Institutions, GB to discuss AI and its impact on
financial institutions.

Transcript:

ASSEM MARAT: We have an enormous amount of data within the
industry, which, unfortunately, is not used to its full potential.
So if we’re able to deliver a more efficient way of processing
that data, there’s a number of benefits that we can achieve.
The first one would be around actuarial models and how it’s not
just about giving you better pricing points but also giving you a
better risk assessment tool.

SPEAKER: Welcome to All Eyes on FIs, a podcast series from the
WTW Financial Institutions team. Our experts have their eyes on
risk management, regulatory changes, and coverage challenges faced
by financial institutions of all kinds and sizes, from professional
liability, to crime, and everything in between.

TRENTON MCNEE: My name is Trenton McNee. And I’m the fintech
and digital assets industry leader for WTW’s UK Financial
Institutions team based in London. Today, I’m joined by Assem
Marat, who is a senior FI underwriter from Starr Insurance, also
based in London.

For those that may not be aware, Starr’s London branch
started writing FI business in 2019 through both its company and
Lloyd’s syndicate platforms and works exclusively with the
broker intermediary market and serves a wide range of organizations
within different industries. Assem, thank you for joining me
today.

ASSEM MARAT: Thanks, Trent. Great to be here.

TRENTON MCNEE: My first question today is generative AI can be
used to help an FI’s operational efficiency, examples being
adverse selection, the filtering out of bad actors. However, it
could also be a cause for future losses such as AI washing, for
example, if companies are exaggerating their claims to artificially
pump up the share price or to win new contracts, new talent, or to
be acquired outright. Can you please provide your take on this?

ASSEM MARAT: When we look into the financial institution sector
and how AI is being used within it, to broadly speaking and
generalizing, it is to deliver better efficiencies across the
sector by speeding up information processing, and in a more
technical and technological level, by generating better developer
productivity.

When you look at the sector and the participants in the sector,
these are the firms that are primarily focused on providing
services to clients. Client service and customer outcome is really
at the core of what they do. We don’t deal with extraction
industry clients, for example, where we’re digging up goods
from the ground and we’re trying to sell them for the highest
possible price.

A lot of what happens within the sector is about client service.
It’s about how they service their clients. It’s about how
they can process the information to improve that service and/or
identify patterns to make the business better, which ultimately
delivers a better customer outcome, which translates in a better
business performance.

As for the second part of your question concerning AI washing, I
would mark it down to just a change competition environment because
of AI. As it is with any innovation tool that enters any market
segment, that changes the dynamic and how you compete.

For example, maybe a little bit of an outdated example but
probably still relevant is when you look into print and publishing,
once you incorporate a digitization into that sector and the
ability to read magazines and newspapers online, that created a
different competition dynamic and a new dimension for firms
competing in that sector.

So really, it’s the same with AI. And as it is with any new
innovation tool that enhances competition, you’re going to have
examples of companies adapting very well and where it, again,
generated a better customer outcome or a better business
productivity.

And you will see examples where it hasn’t really been
adapted in the same way or it’s more negative than positive,
partially because of the lack of hardware or the lack of
understanding around what the tool represents itself.

TRENTON MCNEE: Thanks, Assem. Yeah, I think for the readers,
they’ll find it’s a net positive from what you’re
trying to say there. This brings me on to my next question. This is
being asked regularly in FI client presentations now. What are you
most concerned about when addressing a client about their own AI
capabilities from a financial risks perspective?

ASSEM MARAT: So financial institutions as an industry is a
heavily regulated segment. And it has always been a heavily
regulated segment. So when we look into the regulatory angle, we
can see that there is already an established framework within
it.

So from a corporate governance and a compliance perspective,
when we look into the actors within financial institutions, we see
a great degree of consistency. There are AI governance committee
that assess business cases. And then there are AI risk and control
committees that then control how those business cases are being
used and implemented throughout the organization.

We also have to remember that for most banks, for example, the
usage of models is not a new concept. And when you think about
models, you have to think about it in the sense of algorithms
that– any algorithms that run automatically. And banks would have
those in the past to assess pricing and capital and risk allocation
models.

So again, the concept itself around what the models are and how
to use them is not new for financial institutions. What they’ve
done is that they’ve taken established framework. And
they’ve modernized it. And they’ve changed it to fit AI and
AI regulation.

And actually, regulation in this sense is helpful because
we’ve seen multiple financial institutions working with
regulators to assess the direction of that compliance and
governance framework and also the degree of changes that they
needed to implement. Banks in particular would have worked very
closely with regulators around things like entitlement.

What we’re really interested in– and this is not something
that we hear a lot when we talk to our clients or we sit on market
presentations– are AI-created exposures. So new exposures that we
potentially haven’t thought of before or existing risks of
different dynamic.

For example, data being the obvious one and the one that we all
are worried about. But it’s not just about how data is stored
and how data is being used and who has access to it, it’s also
around how does an insurer manage data accuracy once they’ve
completed their own model training?

Models use stochastic and statistical ways to figure it out, the
answer to the question that’s been given to them. And no matter
how good the source data is, there will always be a degree of
variability. So it’s not just about inaccuracy, it’s also
about variability and how do you assess the difference in that? And
how do you filter that down to what you really, really trying to
achieve from a model perspective?

So for some organizations, it would be a question more of the
latter around how to filter down variability. And for other
organizations, it would be the question of the former. How do they
get to a point where the source data is really good and accurate?
And for some other firms, it would be both of them.

We also need to remember that within financial institutions, we
do have a lot of unstructured data. That unstructured data is
things like correspondence and trade instructions, compliance log.
And all of that adds complexity into the model training itself and
into the human management component of that.

We also think about fraud and how fraud is changing and whether
we’re going to see increased fraud cases using AI. We’ve
seen some very sophisticated fraud cases in the US and in Australia
with the use of AI.

And again, if we are to assume that AI is a learning tool and
it’s a tool that is ever evolving and changing and giving you
better responses to the same questions asked, so it’s only
natural to assume that the same thing is going to happen with
fraud. The more examples you put about how you’re trying to
perpetrate fraud, the better the ideal for scenario can become.

We also look into things like energy consumption and dependency
on certain hardware but also availability of that hardware,
dependency on certain cloud providers, which we know are not many
worldwide.

And if we look into that further down the line, we always have
the debate around whether we’re using proprietary models or
we’re using open-source models. And that actually in turn will
have an impact on the tech sector, which something maybe for a
different podcast.

Also, there are other factors that we like to think of, which
are the move from how, to what, and why. And that is more in
relation to what is the tech departments and tech services within
financial institutions which are traditionally your very simple IT
support departments. And now coming into front-foot of their
organizations because they are helping the business teams and the
business units to run better informed decisions.

And again, when you talk about developer productivity, they have
to deliver better business outcomes. So they’re becoming more
and more relevant on the day to day of the business than they used
to be.

Again, as a natural from that point around the change
decision-making and how informed decisions are being made, we also
need to think about, how does accountability going to change from a
regulatory perspective?

TRENTON MCNEE: Yeah, thanks, Assem. I could chime in and say
this also impacts employees, shareholders, suppliers, executives,
and customers. Really good answer. And finally, where are the
greatest insurance opportunities for the general insurance
industry?

ASSEM MARAT: Already talked about efficiencies at the very
beginning. And of course, insurance is no exception for that
efficiencies that will be the obvious one. And I think when we
looked into how and where we can deliver efficiencies, we have an
enormous amount of data within the industry which, unfortunately,
is not used to its full potential.

So if we’re able to deliver a more efficient way of
processing that data, there’s a number of benefits that we can
achieve. The first one would be around actuarial models and how
it’s not just about giving you better pricing points but also
giving you a better risk assessment tool with the ability to
identify patterns, also making certain systemic risks a little bit
more predictable, which is future learning for us in the FIBI
side.

Potentially, we can even use it to predict market cycles, which
can then feed into your business strategy. It can also be used to
create better capital allocation tools and better capital usage
tools, which, again, will be hugely important to your business
performance as an insurance carrier. And then

We can look into the development of new products. Like if you
analyze your loss data and you have the ability to analyze large
amounts of loss data and you can identify gaps in coverage, you
might be able to create new and more innovative products in that
space.

We have also spoken about underwriting efficiencies in the
market where, again, submissions, informations can be processed
quicker. And the AI model can help you identify red flags straight
away.

We do have a couple of examples of automated underwriting
platforms. So again, AI can be used to deliver some further
sophistication to those. It can also lead to a more sophisticated
way of underwriting because you can also tie a lot of externally
available data or a lot of publicly available data. And the obvious
one would be everything that is out there around US litigation.

TRENTON MCNEE: Thanks, Assem. And just to summarize today, what
are your three key points you would like everyone to take away from
today’s podcast.

ASSEM MARAT: Firstly, AI is now broadly used within a variety of
industries, not just financial institutions and tech. And the aim
seems to be to extract a net positive business impact.

Secondly, we might see some further regulatory challenges around
that the more the usage of AI is being adopted. We’re also
probably going to see some business infrastructure and operational
challenges, going back to the debate around proprietary and open
source models, for example.

Thirdly, AI is most definitely, again, going to get bigger and
has a potential of significant impact on general economic
components like production, consumption, and exchange activities on
a larger scale.

TRENTON MCNEE: Thanks, Assem, for your time today and for those
who have listened to this podcast. Look out for our next podcast
episode in the All Eyes on FIs series.

SPEAKER: Thank you for joining this WTW podcast featuring the
latest thinking and perspectives on people, capital, climate, and
risk in the financial services industry. For more information,
visit wtwco.com.

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