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.