Data Science, Financial Services, Insurance, and Innovation (CXOTalk

I think we need to start this conversation
with some background about the insurance industry to give us context around how data science
is used. Murli, share with us about the insurance industry,
and what do we need to know in the context of data science? Certainly. The core challenge for the insurance sector
is similar to some of financial services. In insurance you’re trying to predict your
cost of goods sold at the point of sale. Getting that right is absolutely critical
in your ability to achieve margins down the road. Anything and everything that you can do to
understand that at its core will give you a significant competitive advantage. Now if you zoom out from that problem statement,
in general there are many similarities in insurance other industries around the role
of data science and machine learning in augmenting human intelligence and making better decisions–more
structured, granular, sophisticated, consistent decisions–in sales and marketing, as well
as in pricing, underwriting, and in claims, which is a significant part of the fulfillment
of the promise that insurance carriers make to their customers. What we call data science today is really
part of a long history of the application of mathematics and computing to industry. When I joined the industry, and I started
my world in finance at Wall Street, back then we used to call these jobs quant roles. You would figure out how to trade in capital
markets, make predictions about which way the stock price would move. I think what we’ve seen is that the tools
and the technologies that we used there were then really adopted in Silicon Valley, really
turbocharged, frankly made, actually, much more usable. Then the cost of computing made it so that
you could apply this not just to a few select problems on Wall Street, but all over main
street, all over the rest of the financial services industry. Really, if we zoom out, as Michael was just
describing, can you talk about some of the similarities between data science in the insurance
industry and other non-insurance data science applications as well, since it seems there
are a lot of commonalities there? Most certainly. The first big dissimilarity, so to speak,
when comparing insurance to other sectors is that the role of the actuarial profession
dates back to the early days when insurance was actually created as a sector. The role of analytics in insurance has largely
been driven by the actuarial function, which brings a certain set of nuanced competencies
and capabilities that are relevant to insurance. The challenge has been that if you were to
think about the broader role that data science could play in particular in the world that
we live in today in insurance, you can actually fundamentally reshape human judgment when
it comes to sales, when it comes to underwriting judgment, and even when it comes to claims
through the lens of data and technology in ways that might not have been feasible 10,
12, 15 years ago. The similarity lies in the fact that, much
like many other sectors, in insurance you’ve got a sales or distribution channel. You’ve got a product channel that is around
pricing the product. Some of that is around your cost of goods
sold, and some of that is trying to understand the market’s appetite and the customers’ demands,
so to speak, or demand elasticity, if you would. Last, but not the least, you’ve got the fulfillment
of that promise that you’ve made that is very, very data rich, so if you break down that
value chain to its core elements, there are similarities to other sectors. Now the difference could be that if you think
about healthcare, for instance, healthcare is much more of a transaction, data rich industry
perhaps compared to insurance because you’re engaging with the customers on a very consistent
basis, just as you are in financial services, in banking, and credit cards and such. The different perhaps between insurance and
these other sectors is, while certainly getting your cost of goods sold right early on is
absolutely critical, you’re not necessarily as data rich, as transaction data rich, as
some other sectors are. Right, but you see this with retail. You see this through the smart phone, and
we were doing a lot of that when I was at Foursquare trying to make that retail brick
and mortar experience a bit more digital through your smart phone. You see this all over the place. I think that that’s going to be a major driver
of a lot of consumer electronics that you’re going to see coming up is the need for companies
to have data is going to drive a lot of those interactions onto smart phones, tablets, [and]
wearables. To build on what you just said, Michael, if
you were to contextualize that to insurance, where I see the big leap in innovation happening
in the next two to three years is around this notion of making much more granular, real
time decisions on the basis of machine learning and by really defining data not just in the
traditional internal structured terms, but thinking of it in four quadrants: internal
and external on one dimension, and structured and unstructured on the other dimension. The ability to build machine learning algorithms
on some of these platforms will reshape what humans do in terms of decision-making and
judgment and where models harmonize or balance human judgment with machine intelligence. The way I would frame it is oftentimes people
think of it as an either/or. But if you were to re-paraphrase machine intelligence
as nothing but the collective experience of the institution manifested through some data,
what it does is brings more consistency and granularity to decision-making. That’s not to say that it would obviate the
role of human judgment completely, but it is to say that that balance, that harmony
should and will look dramatically different two years, three years from now than it has
for the last decade and before that. The next big step-change that I see for this
sector as a whole is evolving from a predictor of risk to an actual risk partner that can
actually mitigate outcomes through the power of real time insights. The most obvious example of that is the role
that sensors can play in providing real-time feedback to drivers of vehicles in a way that
hopefully reduces risky driving and mitigates the likelihood of accidents. To me that is the true power of data science
in insurance. The beauty of that is not only does it mitigate
accidents from happening, or adverse events from happening, but what it does in doing
so is reduces the cost of insurance and expands the reach of insurance to a much broader population,
both in the developed and developing world. To me, that’s a beautiful thing if you think
about society having a much higher level of financial protection across every aspect of
our lives. If we think about what’s new in data science,
that is, why is data science different from or how does data science expand upon things
like the actuarial tradition, like statisticians, the quants of yore , I think it really does
kind of come down to this idea that, one, we’re using not just structure data, so it’s
not just SQL queries any more, but it’s semistructured and unstructured data. How do you start handling things when they
don’t come in nice tables that you can load into Excel or that you can put into SQL? We are also in a world where data is much
larger. You mentioned telematics. If you were taking a reading off of every
car every second, that’s a lot of numbers you’ve got to store, and that’s a very different
paradigm for computation. You start having to think about, how do you
store this data? How do you deal with data now that it’s stored
across multiple computers? How do you think about computation in that
context? Then of course the last thing is always this
idea around real time data. I think that analytics has historically been–you
might call it–kind of a batch process. Run it once; generate a report; show it to
people; you’re done. Now it’s a continuous process. You run it; you have to instantly find the
latest trends; put that into production so that you can adapt to that in an intelligent
way; and then do that again the next hour, the next minute. That’s kind of where competition is driving
you. If you look at what Silicon Valley has been
doing, it is very much your server is constantly learning from user behavior and then able
to adjust how it interacts with users in a way that–to borrow their expression–delights
the user. I think that we’re seeing that. Traditional companies, that is non-tech-based
companies, are having to kind of emulate that kind of level of customer service and satisfaction. I think a lot of that comes down to big data
and being able to have a team that’s capable of understanding how to manipulate this new
type of data faster, more data, different kinds of data in a world that’s rapidly evolving. That’s right, Michael. If you think about the historic definition
of transactional data in healthcare and banking, we know that that’s been at the core of how
they think about analytics for quite a while now. Traditionally, most of insurance has not had
that version. But if you were to zoom out and define data
in a much broader sense that includes images, that includes audio, that includes all sorts
of unstructured data, now insurance has its own version layered on top with IoT and such. Insurance has its own version of transactional
data. The ability to harness that and dramatically
change the cycle time of decision-making, as well as the granularity of decision-making,
is where the goldmine is for insurance in the coming five years or so. It kind of comes down to two basic first steps. The first step: get the data, collect it,
[and] store it, what have you. Second step is to find the talent that’s necessary
to deal with the data, manipulate the data, and be able to come up with actionable insights
from that data. If you can do both of those things, then I
think you will be at least taking the first few steps in the direction of building a data
driven culture.

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