Property & Casualty Insurers Reveal Progress With Predictive Modeling Implementation in Towers Watson Survey

  Property & Casualty Insurers Reveal Progress With Predictive Modeling
  Implementation in Towers Watson Survey

Personal lines focus on frequency and severity models; commercial lines on
loss ratio models

Business Wire

NEW YORK -- January 29, 2013

Property & casualty (P&C) insurers acknowledged that the capture and
transformation of data into useful information has turned into a critical
differentiator of performance within the P&C insurance marketplace, according
to a new survey by global professional services company Towers Watson (NYSE,
NASDAQ: TW). Executives from both personal and commercial lines insurers
participated, with a relatively even divide among small, midsize and large

“Where there is data, there is opportunity,” said Brian Stoll, director,
Property & Casualty practice, Towers Watson. “P&C insurers face difficult
long-term competitive challenges, but these can be mitigated for carriers that
efficiently integrate predictive modeling and data-driven analytics into
operating functions across the enterprise.”

The P&C Insurance Predictive Modeling Survey clearly illustrated the
importance of predictive modeling to insurers’ business. Personal lines
carriers nearly unanimously (98%) said predictive modeling is either
essential, or very important, to their business. Eighty percent of small to
mid-market commercial lines carriers agreed. Large commercial accounts and
specialty lines carriers were less convinced overall, with 55% indicating that
predictive modeling is essential or very important to their business.

Competitive Advantage, Usage Variation

The desire to improve profitability emerged as the leading reason why P&C
carriers use predictive models. Ninety percent of all U.S. participants cited
a desire to improve bottom-line performance as the primary reason, followed by
competitive pressure (75%). Larger insurers are actively using predictive
modeling in the pursuit of competitive advantage, while smaller carriers have
been slower to adopt it.

  *For example, among personal auto carriers, 92% of large respondents use
    predictive modeling, a number that drops to 76% for midsize carriers and
    57% for small carriers.
  *The trend also holds true to a lesser extent for standard commercial lines
    such as commercial auto, where the percentage of respondents that
    currently use predictive models decreases from 62% for large carriers to
    6% for small carriers.

“Smaller carriers slower to adopt predictive models are at a disadvantage,
particularly those competing for business directly against the large
insurers,” said Klayton Southwood, senior consultant, Towers Watson. “They
face loss of market share and adverse selection as their larger counterparts
that have implemented predictive models can target better risks and price more
accurately. Smaller insurers need to find ways to follow quickly and leverage
predictive modeling despite their relative lack of scale.”

P&C insurers diverge on ways they use predictive modeling for their business.
Personal lines carriers are more likely to use models for automated renewal
decisions and for pricing than commercial lines insurers. With respect to
claim applications, personal lines carriers are more likely to use modeling to
detect fraud, while commercial lines carriers use it more to triage claims or
to evaluate claims for litigation potential. Dependent variable targeting
differs, too, with nearly two-thirds of personal lines carriers modeling on
frequency and severity separately, and less than one-quarter modeling on loss

Challenges and Priorities

The survey examined insurers’ most pressing challenges for implementing
modeling and ways to improve on techniques. All carriers said they struggle
with data and people challenges when incorporating data modeling into rating
or underwriting plans, but differences emerged by size of carrier. Nearly
three-fourths (73%) of large carriers cited both data and IT resource
constraints as their top two challenges, while nearly two-thirds (64%) of
midsize carriers ranked IT resource constraints and over three-fifths (61%)
listed cultural challenges as their biggest hurdles. Seventy-five percent of
small carriers ranked data as their biggest challenge, followed by people
challenges (55%).

“For large carriers, the sheer volume of customers, producers, data and
business lines, combined with cumbersome legacy systems, may explain why data
and IT resources present challenges,” said Stoll. “Smaller carriers report
data challenges, but don’t voice as much concern about IT resource
constraints. This may reflect how, by comparison, the challenge of making
better use of their limited data dwarfs other concerns.”

Insurers’ top priorities for improving their modeling techniques differ by
line of business. Personal lines carriers intend to spend more time focusing
on non-pricing applications, such as development of target marketing lists.
Many of these carriers are ready to extend the use of predictive modeling to
gain market share in competitive consumer markets. Conversely, over
one-quarter of commercial lines respondents indicated that monitoring
experience against modeling results will be a higher priority in 2013, in
addition to enhancing modeling approaches.

Financial Impact

The survey also explored predictive modeling’s impact on insurers’ financial
results. Most carriers have improved their bottom-line profitability through
predictive model implementation; top-line growth impacts have been less
pervasive. Personal lines carriers have realized more benefit from
implementation in all measures of top- and bottom-line performance. Midsize
and large carriers reported significantly more favorable bottom-line impacts
from predictive modeling, particularly in the areas of loss ratio improvement
and profitability.

The time elapsed for respondents to actually see financial results varies
significantly, with top-line impacts on performance emerging more quickly than
those for the bottom line. While most participants said they begin seeing
revenue results in a year or less, the time to realize net income results is
frequently two years.

“The lead time to realize both top- and bottom-line impacts tends to be much
longer in commercial lines. It will be interesting to see whether these times
will shorten as carriers continue to feel more comfortable with predictive
modeling efforts and become better versed in implementation,” said Southwood.

About the Survey

Towers Watson’s fourth annual Predictive Modeling Survey included executives
from U.S. and Canadian P&C insurers. A total of 63 U.S. and nine Canadian
executives responded. Responding companies represent a significant share of
the U.S. P&C insurance market for both personal lines carriers (17%) and
commercial lines carriers (21%). Respondents were relatively evenly divided
among small, midsize and large P&C insurers, as measured by 2011 annual direct
written premium, and between carriers that primarily write either personal
lines or commercial lines business. Roughly 13% of respondents split their
business evenly between personal lines and commercial lines. With its range of
advanced analytical and modeling software, Towers Watson helps insurers
improve pricing performance through predictive modeling.

About Towers Watson

Towers Watson (NYSE, NASDAQ: TW) is a leading global professional services
company that helps organizations improve performance through effective people,
risk and financial management. The company offers solutions in the areas of
benefits, talent management, rewards, and risk and capital management. Towers
Watson has 14,000 associates around the world and is located on the web at


Towers Watson
Josh Wozman, +1 703-258-7670
Binoli Savani, +1 703-258-7648
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