This document discusses how analytics are driving strategic objectives in the insurance industry. It outlines how insurance relies on statistical analysis and has benefited from accurate risk assessment technologies. The core insurance product is based on complex pricing and risk algorithms. The document then summarizes how analytics are used across key areas like attracting and retaining customers, investments, operational risk, and staff functions. It provides examples of strategic objectives from a large personal lines carrier focused on customers, excellence, growth and risk management. The conclusion states that analytics enable improvements across tactical activities but must track outcomes like customer retention to justify business cases and minimize risk.

1. March 2014
Keeping in Step
with Strategic
Objectives through
Analytics
help every carrier benchmark and
improve their performance.

Insurance has driven statistical analysis
based on parameters that have been
hard to quantify. Risk management has
become a highly refined science, and
has benefitted carriers and customers
alike due to highly accurate
technologies that detect fraud,
minimize adverse selection, keep prices
competitive and defines the risk
appetite and tolerance levels in each
company.

Unlike Retail, Manufacturing,
Hospitality or even banking, Insurance
products are not tangible. The payoff
comes as a result of a sustained loss.
This has meant that the core product in
Insurance has been always a financial
construct based on a future event, and
is therefore the result of complex
algorithms that compute pricing, risk,
historical loss, fraud, reserving and
other factors. Along with technologies
that address day to day operations, this
core product has been largely a
beneficiary of high automation,
minimizing human error. Indeed,
human error has not been eliminated,
and is a big part of operational risk,
which is constantly being addressed by
ever-changing regulation and selfpolicing.
Vijai John
Along with Banking, Financial Services and
Capital Markets, no other industry may have
received the attention of myriad groups of
people- administrators, data scientists,
strategists, economists, academics,
technologists, efficiency gurus, customer
experience experts, legal experts and many
others- than the Insurance industry. An industry
that has always placed its foundation on
weighing the possible negative outcomes
against the willingness of customers to hedge
their risks, Insurance has relied on and refined
statistical analysis, customer behavior, fiscal
prudence, technology and operations over
centuries.
This industry has also seen heavy regulation
globally, addressing several aspects of its
functions and the risks they entail. The industry
has also largely undertaken to set up standards
for itself through associations that work closely
with the regulatory authorities. In this process
of adherence to regulation and self-policing,
many aspects of this industry have reflected
refinement that exceeds that of others.
Some key components of this refinement have
been brought about via the following channels:

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Key data relating to operating
performance, risk, finance and
investments are reported to regulatory
authorities, which are in turn shared
with private players who analyze such
data to derive meaningful insights that
For an industry that plays in the DMZ between
the fears of customer about sustaining a loss
and the reassurance of a universe of
policyholders which has historically held up in
most time-tested product lines as being a
bulwark against total ruin, it may be surprising
that Insurance is itself always driven by the
bogey of risk. Nearly all technological or
operational change in Insurance has been
driven by the desire to avert risk. Business
process improvements aimed at speed to
market, customer retention, pricing accuracy

2. March 2014
and other outcomes have their basis in
minimizing risk. A host of analytics solutions
have been aimed at addressing the data that
has been gleaned from Insurance operations.
Most analytics in Insurance have focused on the
following categories of operations:


customer data, an understanding of
win-back activities for customer who
are about to lapse, focusing on the
lifetime value of customers and
reducing the cost of servicing customers
can mean improved retention and
lower loss ratios.
Distribution: Whether via push (agents
and brokers) or pull (digital) strategies,
distribution has relied on accuracy and
speed in analytics.
Social Media: This source of data has
strengthened real time analytics and
enabled sophisticated hypothesis in
specific use cases.
INVESTMENTS:

ATTRACTING, RETAINING, BENCHMARKING
AND RATIONALIZING CUSTOMERS:




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Customer Segmentation: Advanced
customer analytics can help identify
overlooked customer segments or
redefine the risk profiles of customers.
Identifying the right customers: Not all
customers are equal. Use predictive
analytics in Marketing to direct sales
efforts to only customers within a
defined risk tolerance level.
Analyzing marketing campaigns:
Analytics and reporting tools provide a
clear picture of marketing campaigns
across the enterprise, response rates,
types of responses and conversion
rates.
Cross and up sell: While cross selling
and upselling have always needed more
than just customer analytics, this aspect
of sales has caught the attention of
service providers and technology
vendors powerfully. Besides the ready
and timely availability of product and


Portfolio Management and
Optimization: From pricing an asset in
relation to its market risk to
optimization a portfolio of assets,
analytical models that use available
data have proven invaluable to
Insurance companies.
Minimizing Risk of Liquidation:
Analytical tools have made it easier to
posit growth and expenses associated
with an asset and therefore schedule
asset sales and payments appropriately.
Financial and Probability Modeling:
Mergers and Acquisitions, Capital
budgeting, capital-intensive projects or
investments have all been enabled via
financial modeling. Although
traditionally based on spreadsheets,
new tools have made these tasks
quicker and more accurate.
STAFF FUNCTIONS OR SHARED SERVICES:

Human Resources: Talent Analytics has
been making news. There have been
several surprises relating to employee
performance, retention and lapses
based on data points that had not been
thought of earlier.

3. March 2014


Corporate IT: IT Performance
Management has come of age. This
function has been slow in changing in
many enterprises due to the myriad
responsibilities that a CIO faces. Slowly
but surely many IT organizations are
adopting the best practices around
program management, outcome-based
tracking of services, adoption of ITIL
and other guides to streamline
performance. All of these are strongly
backed by reporting tools that have
transformed organizations.
Corporate Finance: Financial close,
budgeting, forecasting and financial
statement analysis have all benefitted
from analytical tools that have been
designed to make the process faster
and accurate.



OPERATIONAL RISK:



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Product Development: Analytics have
been crucial from design through
development and deployment of new
products, their success hinging on an
accurate assessment of needs and
speed to market.
Price Optimization: Besides pricing,
price optimization analytics has been
useful in deriving negotiating points
with customers and prospects through
changes to cover and price discounts.
Underwriting: Recent advances have
enabled underwriters to focus closely
on policyholder characteristics which
had been overlooked in the past. A
recent report focused on how the 2009
recession had forced many people into
making choices that would be
considered less than ideal for an
underwriter to issue a homeowners
policy. However, due to the nature of
the circumstances, possibly
compounded by a job loss, such an
incident may have been an aberration.
Underwriters have turned to several
methods, including analytical tools that

provide more nuanced insights to
identify creditworthiness.
Telematics: Analytics based on driving
habits and patterns have enabled
carriers to design usage-based
insurance policies.
Catastrophe: The incidence of highly
impactful catastrophes in the first
decade of the 21st Century has given
fresh impetus to CAT modeling. Many
carriers have now turned to risk by peril
models for homeowners and
commercial property insurance.
Fraud Fighting: Analytics in fraud
detection has also gained ground in the
recent years. Largely, carriers have
fought fraud mainly due to the diligence
of experienced adjustors who could
recognize patterns clearly. Much of this
knowledge has now been transferred to
analytical frameworks which are
constantly improving.
Reserve Development: Historic data,
claims analytics and predictive
modeling based on multiple parameters
have made it possible to predict
probabilities for loss more accurately.
Not surprisingly, an examination of the stated
strategic objectives of several Insurance carriers
has reflected improvements in the above
categories.
One of the largest personal lines carriers in the
US states its operational objectives as follows:





Customer focus
Operational excellence
Enterprise risk and return
Sustainable growth
Capital management
While such statements are qualitative in nature,
a closer look at activities that are filtered down
as a result of these reveals that analytics drive
changes in these enterprises.

4. March 2014
When considering a project at a carrier or
planning to sell to a carrier, it is useful to keep a
high-level goal in mind.
Customer Retention could be the goal for any
number of customer focused activities. This
approach from the top presumes that tactical
goals can wait if they do not substantially
impact the clearly metric-driven objectives that
the enterprise would embrace. To improve
customer retention any of the following, more
tactical activities would help.



Contact Center Efficiency Optimization
Customer Experience Management by
tracking NPS scores and identifying
pitfalls in the customer engagement
process
Improved technology that provides
greater visibility into customers for
agents, sales, service and support
personnel
However, without a determined effort to track
data back to the outcome of Customer
Retention, these efforts will remain tactical.
Indeed, for an executive to justify the business
case for such engagements would mean an
understanding of ultimate financial gains and a
significant minimization of risk.
Improving speed and accuracy in underwriting
has obvious benefits in a competitive market.
Improvements in process, technology or
operations can often create a differentiator in
this area. As with other areas, this too is a
candidate for benchmarking for risk and return.
In Insurance a distinction of functions into cost
and profit centers is often simplistic, as cost
centers are often overlooked revenue drivers.
The availability of data from the industry and
the systematic measurement of a carrier’s
internal data should mean that the industry
must benefit from a solid strategic perspective
this provides. However, this is far from being
the norm.
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Our analysis of reported data reveals specific
opportunities to improve in carrier’s operating
performance. A ranking of product lines by
underwriting risk reveals the risk appetites of
carriers based on their product portfolios and
their exposure to specific products. Geographic
analysis (state-wise) of carriers’ key loss and
expense ratios illustrates their ongoing
performance, and may point to the prevalence
of fraud and the efficacy of a carrier’s fraud
detection efforts. Multivariate analysis based on
different parameters validates these
assumptions.
While a carrier may have sophisticated tools for
fraud detection, an understanding of industry
trends strengthens this effort, and may help
uncover fraud at the inception of a policy as
well as fault-lines in loss adjustment,
underwriting practices and marketing.
Similarly, our analysis of underwriting
performance of over 250 carrier groups in
homeowners insurance reveals categories of
carriers experiencing different levels of loss
ratios. Besides fraud, this may reveal an
opportunity for automating part of the
underwriting process. This data led our
Property and Casualty solutions team to create
a solution to automate homeowners insurance,
cutting down on the time and increasing pricing
accuracy based on data that is now available via
high resolution pictures and feeds from other
sources relating to home details. The solution,
structured on a rules-based framework, is
among a new breed of solutions seeking to
upend the manual process that has been the
norm for long.
The Insurance industry, as already mentioned, is
self-policing. The improvements that are being
made are in response to changing threats and
new opportunities that are presenting
themselves rapidly. Due to the availability of
enormous amounts of data as well as new
technologies it has become possible to
manipulate this data into outcomes that are
derived from strategic objectives. An

5. March 2014
architecture that begins from these outcomes
can go a long way towards deriving value from
tactical initiatives in technology and operations.
For more information, please contact:
Vijai John
iGATE
vijai.john@igate.com
(630)335-1808
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