Data Driven
An
explosion of data and advancements in analytics is shifting underwriting from
an art to a science.
KATE SMITH MAY 2019
Key Points
·
Clear
Picture: Underwriters are
gaining more dynamic views of risk through new sources of data and advanced
analytics.
·
Model
Client: By showing
underwriters which policies will be more profitable, predictive models allow
for more risk-based pricing.
·
Reality
Check: Overreliance on
new tools is dangerous because results are only as good as the data being used,
and experts say data is still poor.
Having grown up in Pennsylvania, Kassie Bryan
knows well that one of the biggest road hazards in the state has four legs.
“I saw so many deer accidents growing up
there,” Bryan, head of P&C Solutions for Swiss Re, said.
So it made perfect sense to Bryan when Swiss
Re incorporated deer population density data into its Motor Market Analyzer, a
predictive model that uses granular data sets representing accident risk
factors to determine accident frequency and severity by geographic area.
“We did a project for a client who was looking
to grow into the state of Pennsylvania,” Bryan said. “They had no historical
loss data on auto in Pennsylvania, and they wanted to understand what the
drivers of loss are and what they should be thinking about when they enter the
state. One of the external data sets we used was deer population density.
“When I heard we were using deer population
density, I thought, 'That makes so much sense. That's absolutely a driver of
the risk.'”
Advanced analytics enable insurers to paint a
dynamic, and real-time, view of risk. And at the heart of analytics lies data.
Insurers are accessing a wealth of traditional
and nontraditional data sources—from credit ratings and motor vehicle records
to Yelp reviews and, in Swiss Re's case, deer population density—to inform
decisions, improve pricing and increase efficiency.
When combined with advanced technologies and
new modeling capabilities, data becomes an exponentially powerful tool that
enhances an insurer's understanding of risks.
“You're seeing a move from static information
that gets updated once a year or less, to a dynamic view of businesses or
consumers,” Kirstin Marr, president of Valen Analytics, said.
The implications for underwriting are
significant. Not only are data and analytics enabling the automation of certain
parts of the underwriting process, but as data becomes increasingly reliable
experts say underwriting will become more of a science than an art.
“Today, underwriting is manual and highly
experiential,” Ari Chester, a partner at McKinsey, said. “It's based on
experience and hard-learned lessons from individual underwriters, who learn
from apprenticeship. There are analytics and tools that are used, but they're
often homemade, homegrown and inconsistent. And the data that is used is very
often what is supplied in the submission, which is collected through a broker
or agent from a client.
“In the future, when it's more science-based,
the quality and collection of data will be more advanced. There will be more
tools. There will be more rigor, more use of models and certainly less manual
processing.”
While the use of advanced data analytics is in
the nascent stage, experts say it's already having an impact on efficiency, as
evidenced by the rise of digitally augmented underwriting.
“Parts of the underwriting process that can be
automated are being automated,” Risa Ryan, head of strategy and analysis for
Munich Reinsurance America, said. “And the data is being enhanced with data
sources that go far beyond what we typically have used in the underwriting
process.”
You’re seeing a move from static information
that gets updated once a year or less, to a dynamic view of businesses or
consumers.
Kirstin Marr Valen Analytics
Predicting
Profitability
Data and analytics are not new for
underwriters. Actuarial analysis is grounded in those things.
“When you think about how data has informed
underwriting, which is risk selection and pricing, it's largely been leveraged
at the large scale by actuaries setting rates and selecting company tiers,”
Marr said. “Those are things that are done once for the year and set.
“The other way underwriters have used data is
by getting it from the application or by using manual reports. If it's
commercial auto, they're pulling a motor vehicle report. They've done
inspections. They've relied on agents to validate the business. Those are very
expensive, very manual ways of acquiring and reviewing data.”
Ryan described that process as “inefficient.”
“Now information is being stored in a manner
that is easily accessed and cheap,” Ryan said. “It's less expensive to access
data than it used to be. And we also have the tools to access data; computers
are so fast now. And we've got open-source tools that have democratized the
accessibility and usability of data.”
The end result is an automated, real-time use
of advanced data and analytics in the underwriting workflow.
“We have the ability to intake data in real
time—transactional, primary sources of data,” Marr said.
Rather than relying on data aggregators like
D&B or InfoUSA to validate a business, Marr said, underwriters can use
nontraditional sources of data to build a more robust picture of a business.
“Today you can find a business on Google, you
can see their Yelp reviews,” she said. “Because computing power has advanced so
much, you can leverage the public databases that were really hard for companies
to be able to use. You can go to NOAA and get GIS satellite data. There are
free satellite data sources that are available, free weather sources that are
available. There are all these primary sources that used to be managed by those
large data aggregators, and they're more available now. You have the ability to
manage that yourself, if you want. That's the more advanced use of data in
terms of technology.
“It also allows you to access more
transactional or behavioral data. By scraping Yelp and getting reviews of a
business, you get a sense of sentiment. You get a sense of the quality of that
business and how they serve their customers. You get things you'll never see
from an InfoUSA or a D&B or one of the credit bureaus. There's a lot more
you can learn that, if you're applying advanced analytics to it, can help you
better assess that risk.”
Advanced data analytics are especially powerful
for liability lines, Bryan said.
“Historical loss data for liability business
is sparse,” she said. “Data emerges slowly and it quickly loses credibility
because of the constantly changing risk environments. One of the ways we're
addressing that is through forward-looking modeling, which includes an
exposure-based model that anticipates and incorporates changes in the liability
risk landscape into the quantitative assessment of risk. It uses external data,
not directly insurance-related data, to describe the world that generates the
accidents without having to wait for the accidents to happen.
“We all know that what happened in the past is
not necessarily what will happen in the future,” Bryan added. “So advanced
analytics lets us take advantage of real-time data and advanced computing power
to model the exposures of the future and quantify the risks they will produce.
We're forming a picture of the world that is causing the losses.”
On the property side, publicly available data
sources give more information about particular risks.
“The industry has now got information about
square footage of buildings, flights of stairs in buildings, what the roofs are
made of, how many windows,” Ryan said. “From an aerial imagery standpoint, we
can see the amount of open space around a building. We're actually looking at
that information for underwriting on the liability side. What obstacles or
hazards are around the perimeter of a building? Those are types of information,
especially the pictures, that we didn't typically access in the underwriting
process.”
Ryan said census data, credit scores,
LexisNexis data, demographic data and financial information also can be
valuable in building models.
“Those data sources have been in existence for
a long time, but they have not been as easily accessible as they are now,” Ryan
said. “We are constantly updating our models with the newest information
available. That helps us inform our underwriting decisions here internally, but
it also helps us build models that influence our clients' decisions.
“We've built a risk score model that helps our
clients understand which policyholders will be more profitable than others.”
Marr said that's the beauty of predictive
analytics.
“Instead of having to wait 18 months for a
policy to mature in order to know if it was profitable or not, you can know in
advance where your profitability is heading,” she said. “It allows carriers to
align price to risk in a way that's really hard to do without predictive
analytics. It allows you to do real risk-based pricing.”
Advanced analytics lets us take advantage of
real-time data and advanced computing power to model the exposures of the
future and quantify the risks they will produce. We’re forming a picture of the
world that is causing the losses.
Kassie Bryan Swiss Re
Future of Underwriting
Another consequence of data and technology
advancements is improved underwriting efficiency. Carriers are using digitally
augmented underwriting to automate parts of the process.
This combination of human underwriters and
artificial intelligence-based programs can take several forms, Jeff Heaton,
vice president and data scientist at Reinsurance Group of America, said.
“On one end of the spectrum, it is AI programs
performing the entire underwriting task and referring cases to human
underwriters when the program is unsure,” Heaton said. “On the other end of the
spectrum, it is simply using the AI program as an assistant to the human
underwriter.”
Last October, RGA launched its “AI-Augmented
Underwriting System” with the goal of creating greater efficiency.
“An underwriting file can contain a great deal
of information—much of it duplicated,” Heaton said. “The augmented underwriting
system being produced scans this underwriting file and identifies key
components, such as individual health records, motor vehicle records, and all
places that critical information about the insurance applicant are presented.
These sections are compared and checked for consistency. This allows the
underwriter to quickly navigate to the appropriate parts of the underwriting
file.”
While increased profitability is the primary
goal of advanced data and analytics, certain underwriting lines, such as small
commercial, need the efficiency gains just as much.
“In the small and midsize commercial lines,
you may have a loss ratio that's generally OK, but the challenge is someone is
touching the policy too much,” Chester said. “You have a human doing a lot of
underwriting and administration on a policy that's $2,000 or $5,000. So even if
the accounts are profitable, the level of effort going into it is way too high.
“Of course there's relatively more value in
the loss ratio. But really the analytics and the data are enabling a big
expense improvement by allowing the underwriter to reduce manually underwriting
the $5,000 account or the $1,000 account. Those can be mostly underwritten by
algorithms. And then the underwriter of course, will review the portfolio, look
for trends, monitor adequacy, and continuously contribute to recalibrating and
updating the algorithms. But they're not actually sitting there and manually
trying to assess and underwrite thousands of small accounts.”
Chester does not see automation or artificial
intelligence replacing the human underwriter any time soon, particularly in
commercial lines. But he does see the underwriter's role shifting in several
ways.
“There will be less administrative and manual
work and more thinking,” he said. “We like to compare it to a physician who has
a nurse or a physician's assistant come in and check blood pressure, take your
weight, capture your medical history. Doctors have become more focused on
patient assessment and less on the prep. Underwriting is going to be similar to
that.”
Chester also anticipates primary insurance
underwriting will look more like reinsurance underwriting.
“Reinsurance is based on portfolio-based
underwriting. It's looking at accounts in the aggregate,” Chester said.
“Primary and individual risk insurance will become more like treaty
reinsurance, where you're looking at every individual risk and underwriting it,
but you're also looking, almost in real time and in an integrated way, across
the portfolio. You're taking a dual approach to look at the risk and its place
in the portfolio at the same time.”
The day will come when underwriting will be
fully automated and computers, through machine learning, will be able to notice
trends and update algorithms in real time. But that is still far off.
“As data gets better and as the algorithms are
tested, there will be a time when decisions can be mostly, or even exclusively,
data driven,” Chester said. “Whether it's a decade or two decades away, or
more, it's certainly no sooner.”
Chester said commercial underwriters, at this
stage, shouldn't be overly reliant on new tools and models. The art of
underwriting, he said, is still at least as important as the science.
“It's important to not have too much faith and
too much hope in what the tools and data can provide,” Chester said. “If there
is a tool or model built that gives you an answer, some companies fall into the
trap of giving that answer more credibility than it should have. They've taken
the emotion and human judgment out of the equation. But the answer provided by
the algorithm is not necessarily accurate. It may be that the human judgment
and the imperfect, emotionally driven decision might in fact still be superior
to what the algorithm can produce. The reason is, the output is only as good as
the data, and the data quality is poor.
“Second, risks change and they change pretty
frequently. Algorithms today do not have the sophistication to proactively
learn and adapt until new loss experience is already apparent. Third, it's not
like personal lines where there's a very high volume of mostly homogenous
risks. Even in small commercial, you still have a lot of heterogeneous risk. So
it's easier said than done to build these algorithms that can provide insight
around a particular risk in a seamless way.”
Learn More
Swiss Re Ltd. (A.M. Best # 058838)
Munich Reinsurance America (A.M. Best # 000149)
Reinsurance Group of America (A.M. Best
# 058089)
For ratings and other financial strength
information visit www.ambest.com
Kate Smith is a senior associate editor. She can be reached at kate.smith@ambest.com.
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