Technology's Impact on Property-Casualty Insurance Operations

C© Risk Management and Insurance Review, 2010, Vol. 13, No.
1, 85-109
DOI: 10.1111/j.1540-6296.2009.01175.x
PERSPECTIVE ARTICLES
TECHNOLOGY’S EFFECT ON PROPERTY–CASUALTY
INSURANCE OPERATIONS
Robert Puelz
ABSTRACT
The post-Glass–Steagall era has presented insurers with new
opportunities and
risks during a time when information flows and business
processes are be-
ing impacted by changing technology. In this article, we explore
how insurers
use and perceive current technology to carry out their
operations by report-
ing results from a sample of insurers that includes some of the
nation’s largest
property and casualty insurers. We find among insurers in our
sample that an
online channel is having a significant impact on customer
retention and rev-
enue enhancement, but a lesser impact on cost reduction.
Interestingly, about
two-thirds of our sample has experienced an increase in their
overall number
of transactions following the adoption on an online channel.
Moreover, while
the Internet is perceived as giving marketing benefits it is not

being used as a
substitute for agents. We find that 65 percent of respondents
have used technol-
ogy to integrate customer data across functional areas and
another 23 percent
plan to do so in the next 3 years. Nearly 71 percent of
respondents have or plan
to adopt service-oriented architecture in their technology
infrastructure.
INTRODUCTION
One of the tasks of insurance academicians is to help
stakeholders and students of the
industry understand the functioning of insurance markets, the
risk transfer that takes
place, and the business proposition of how one can maximize
the wealth of an insurer’s
owners. Structural shifts in business occur for reasons
attributable to knowledge, cre-
ativity, and vision; technology is often a catalyst that nurtures
new ways of thinking.
The following encapsulates one insurance executive’s thinking
about the industry:
Among the student body are many who will be in the next
generation of leaders in
the insurance industry. They can look forward to a career with
even more stimulating
challenges than the industry offers today. There will be fewer
people doing things that
machines can do and more people doing those important things
that only people can
Robert Puelz is the Dexter Professor of Insurance, Edwin L.
Cox School of Business, Southern
Methodist University, Dallas, TX 75275; e-mail:

[email protected] I am grateful to Jerry
Johns of the Southwestern Insurance Information Services, and
my communications with David
Repinski of Cunningham & Lindsay, Mike Reid of Liberty
Mutual, Jim Snikeris of Farmers, and
James Lankford of Texas Farm Bureau. Finally, thanks to
Robert Quirk and Henry Wyche for
research assistance. This article was subject to double-blind
peer review.
85
86 RISK MANAGEMENT AND INSURANCE REVIEW
do. The most challenging aspects of these electronic methods
are the human rather
than the mechanical—the decrease in routine tasks; the varied
new skills which are
needed for the new jobs created; and the growing importance of
research, analysis,
organization, and planning. There are truly interesting years
ahead for all who are so
interested in insurance.
The quote appeared a half-century ago in the Journal of Risk
and Insurance and its ap-
plicability today is remarkable. Indeed, it could be argued that
some insurance firms,
caught in a managerial stasis of thought, would do well to heed
the call by Bagby about
the “growing importance of research [and] analysis.” For some
insurers their internal
structure has remained settled over the years with the areas of
pricing, underwriting,

and claims the predominant functional areas that define this
business form. Taking
an appropriate risk for a given price then honoring the contract
when a loss occurs
is the essence of value provided by insurers. While we know
risk transfer is as old
as the “contract of Bottomry” included in the Code of
Hammurabi, the recent changes
in the legal environment and unprecedented technological
innovation present oppor-
tunities not seen by insurance managers of the past.1 Gramm–
Leach–Bliley (Financial
Services Modernization Act of 1999) has given a structural
opportunity to other financial
institutions to enter the insurance business and vice versa.
Optional federal chartering
of insurers as an alternative to state regulatory regimes is an
idea that has not yet gained
significant traction in Congress but affords the opportunity to
create an insurance envi-
ronment with more flexibility, choice, and competition.2
Relaxing legal strictures offers
the potential for an unencumbered, more diversified financial
environment. Perils exist
for current management, however, since stakeholders expect
more flexible thinking.3
Staid and mature insurers and their management teams are not
likely to exist in a more
traditional form as new competitors enter their market;
consequently, insurers ought to
be ripe for new ideas that develop profitable lines of business
and control costs. How
an insurer has used technology to enhance a functional area or
its integration with
other operational components likely reveals the wisdom of

management in enhancing
shareholder value.
The process of managing workflow is part strategic, part
administrative. While the Inter-
net may be used to receive marketing inquiries, some companies
with exclusive agency
arrangements weigh the benefits of direct marketing
communications against disenfran-
chising the existing distribution channel. Thus, for example,
Texas Farm Bureau, which is
a rural insurer with about 180 offices spread throughout Texas,
takes an Internet inquiry
and feeds it to a member of its captive agency force.4 Once the
application is taken, the
process is automated with technology beyond the Internet
playing a role. Agents submit
1 A contract of Bottomry dates to Babylonian times where loans
were forgiven if a ship suffered
a robbery loss while in transit. If the ship’s journal was
uneventful, the interest charged on
the loan was higher than normal market conditions; in other
words, it included an insurance
premium (see Trenerry, 1926).
2 Optional federal chartering of insurers has been studied for
life insurers (see Bair et al., 2002),
and England (2005) has provided a more general synopsis on the
topic that includes numerous
references to the work of Grace and Klein (2000) and
Harrington (2002).
3 The academic literature is turning in this direction, too.
Skipper and Kwon (2007) include a
chapter dedicated to the issue of financial services integration

in their recently released textbook.
4 Thanks to James Langford of Texas Farm Bureau for sharing
the operational process of his firm.
INSURANCE OPERATIONS 87
applications electronically through a company network. The
underwriting process for
auto insurance begins with the raw data being fed into
Choicepoint software that is
given parameters by management for the risk’s acceptability.5
In addition, an electronic
check of the new applicant’s prior carrier activity, credit rating,
and motor vehicle report
is undertaken; an overall profile of the risk is created; and an
electronic underwriting
determination is made. In cases where the applicant does not fit
the profile established
by management, manual underwriting is undertaken as a second
tier of investiga-
tion of a risk’s acceptability. In this example, the technology
advantage over human
intervention is both an error elimination and scale economies
bonus to the insurer’s
operations but, for this form, maintaining loyalty among its
traditional distribution
channel.
A broader view of technology’ s effect on the operations of
property–casualty insurers
is the focus of this article. While a LOMA forum cites that 41
percent of the information

technology budget goes to core, fundamental processing of the
insurance business and
only 19 percent is allocated to channel management, there is the
expectation that IT
will be better utilized to help grow business if systems are in
place that can support
new growth.6 Thus, one question answered by this research is
what are the existing
technologies identified by insurers that will help grow their
business, and where do they
expect growth to occur? An associated question is what are the
existing technologies
identified by insurers that help to service their existing
business? Answers to these
questions are provided in the responses from 17 insurers who
responded to a survey
instrument that focused on the operational impact of
technology.
One of the goals of the article is to move beyond the traditional
siloed approach to
insurance operations and present current management ideas that
take advantage of
technology to modify overall operations. Since insurance
industry profitability in recent
years has been driven by investment performance that has offset
insured losses and
operational expenses, successful methods to minimize cash
outflow or turn an insur-
ance profit may reside in technological innovation. How have
the operational pieces
of an insurance company’s structure become more integrated
through the advent of
technology? What are the key technological innovations used in
practice and how have
their utilization translated to efficiency, market opportunity,

and profitability? The sur-
vey instrument utilized in this article and included in the
Appendix was structured
with support from the industry through interviews and other
communications. While
the analytical approach to this article is necessarily descriptive
the results are revealing
about how the effective use of technology and innovation have
altered the managerial
landscape in the insurance industry.
BACKGROUND TO THIS STUDY
As background, the traditional view of insurance company
operations is encapsulated
in various industry texts; for example, Myhr and Markham
(2004) describe three main
functional areas of an insurance company (marketing,
underwriting, and claims) sup-
ported by nine additional areas outlined in Table 1.
5 See http://www.choicepoint.com/business/pc_ins/pc_ins.html.
6 See http://www.loma.org/res-08-05-SF-anaylsts.asp.
TABLE 1
Breakdown of Functional Areas
Marketing Underwriting Claims
Loss control Reinsurance
Human resources Actuarial

Legal services Investments
Information technology Premium audit
Human resources Accounting
Because an insurer has a well-defined process, the insurance
business model begins with
this structure, running the risk that strategy will be considered
and implemented within
these core silos without considering interactions. Myhr and
Markham (2004) discuss
interdependence in the following manner, “Although each
function within an insurer
must have some autonomy to perform its work, those functions
are far from completely
independent. They must interact constantly if the insurer is to
operate efficiently,” al-
though that is about the only attention these writers pay to the
topic. Trieschmann
et al. (2005) give a different explanation of an insurer’s
operations. They offer the fol-
lowing listing of insurer functions: production, underwriting,
rate making, managing
claims and losses, investing and financing, accounting, and
miscellaneous activities that
involve legal, marketing research, engineering, and personnel
management. Pritchett
et al. (1996) are brief in their description of an insurer offering
the “flow of an insurer’s
operation,” to include: management, actuarial, marketing,
underwriting, administra-
tion, investments, legal, and claims. The intent of this research
is to provide and quan-
tify that broader perspective. One goal of this research is to lay
the foundation for a

refreshed understanding of how traditional operational areas can
be melded together
by technology. This integration of the functional areas,
conceptually depicted in Figure
1, overlays the distinct operational areas upon one another with
technology serving as
the bonding agent or, at least, permitting managers to adopt
technology as a bonding
agent.
THE SURVEY AND THE PARTICIPANTS
The survey instrument was web-based and entailed about 40
questions. The final survey
product had the benefit of input through conversations with
various insurance industry
executives. The instrument was e-mail distributed through both
the Southwest Insurance
Information Service (SIIS) and the National Association of
Mutual Insurance Companies
(NAMIC) in which member companies were invited to
participate.7 The 17 insurers
7 The initial survey emailing was handled by the NAMIC and
the SIIS. The NAMIC represents
“1,400 member companies that underwrite more than 40 percent
of the property/casualty insur-
ance premium in the United States”
(http://www.namic.org/about/default.asp). According to
Jerry Johns, the SIIS represents “about 160 insurers” and “they
write about 60 percent of property
and casualty premiums in Texas and about the same around the
world.”

FIGURE 1
Functional Areas of an Insurer
TABLE 2
Insurance Company Respondents
Texas Farm Bureau Mutual Insurance Company Liberty Mutuala
(6)
Farmers Insurancea (3) Mercury Insurance
Texas Windstorm Insurance Association Magna Carta
Companies
Allstatea (4) Infinity Insurance Companies
American Modern Insurance Group Nationwidea (7)
Service Lloyds Insurance Company State Farm Insurancea (1)
Accredited Surety and Casualty Company, Inc. Travelersa (5)
Hochheim Prairie Farm Mutual Insurance Assoc. EMC
Insurance Companies
Beacon Insurance Group
aCompanies identified as “large” in this study had total assets
of at least $19 billion. The rank of
these companies by direct premiums written in 2006 in the
United States is in parentheses.
See http://www.iii.org/media/facts/statsbyissue/industry/.
that responded are listed in Table 2. While the number of

responding companies has
created a relatively small sample, it does include major U.S.
insurers along with a
number of small insurers. The size effect on technology
utilization is apparent in the
results.
TABLE 3
Importance of Online Channel to Business Goals
Small insurers Average
Cost reduction 2.09
Revenue enhancement 2.18
Customer retention 2.63
Large insurers
Cost reduction 2.17
All insurers
Cost reduction 2.12

FINDINGS: THE ONLINE CHANNEL
A natural starting point for research into the role of technology
for insurers is the role
of the Internet. Our first area of interest was the value
proposition of online distribution
channels. Do insurers view this channel as an opportunity to
reduce costs, enhance
revenues, or better retain their customers? Dennis Campbell
(2003) has written on the
impact of electronic distribution channels in financial services
and finds in his sample of
customers at a single bank that online customers exercised more
transactions while also
more closely monitoring bank activity to save money from
minimum balance penalties.
While Campbell finds that online customers are less profitable
to a bank in the short run,
he also finds that these customers were more reliable revealing
higher retention rates.
In our study survey, participants were asked to assess from low
(1) to high (3) how
an online channel has helped in cost reduction, revenue
enhancement, and customer
retention. The results in Table 3 indicate that from a
management perspective, an online
channel does not have low impact on any of the key value
propositions; the range is from
medium to high, indicating the importance to these three
business goals. In particular,
customer retention appears to be a driving force in insurance
management interest in
an online channel and more so among relatively smaller
insurers. Larger insurers see

revenue enhancement as a key reason for having a web
presence. While both Campbell’s
(2003) results and the findings herein were obtained from
stakeholders who fall under
the financial services umbrella, Campbell’s sample was at the
consumer level and this
survey is at the managerial level.
Interestingly, Campbell’s (2003) findings, that revenue
reduction when technology is
deployed in the form of PC banking, are not mirrored by the
expectation of insurer
management that an online channel will enhance revenue.
TABLE 4
Effects After Adoption of Online Channel
Small insurers Average
Proportion experiencing transaction volume increase 0.73
Proportion experiencing erosion of offline business following
online
channel adoption
0.55
Large insurers

online
channel adoption
0.17
All insurers
online
channel adoption
0.41
To gain further insight into what is driving insurer expectations
for online channel
performance, we asked two questions related to the value
proposition that asked survey
participants to assess their results following the adoption of an
online channel. First,
did total transaction volume increase following the adoption of
an online channel, and
second, was there an erosion of offline business following the
adoption of an online
channel? “Yes” took the value of 1 and “no” took the value of
0. While 65 percent of all
respondents indicated that total transaction volume did increase,
73 percent of smaller
insurers saw gains in total transaction volume. The overall
sample results expressed
in Table 4 are consistent with Campbell’s (2003) findings for

banks. The results are
also linked to the type of traditional distribution system in pla ce
among the survey
respondents.
For example, since Cummins and VanDerhei (1979), there has
been evidence that those
firms that utilize an independent agency system are less
efficient compared with their
exclusive agency, direct-writing counterparts, and Berger et al.
(1997) show that higher
quality services offered by independent agents justify their
higher expenses and par-
tially explain why both types of distribution systems were able
to coexist. An online
channel changes the marketing distribution mix in today’s
environment, and inter-
est among small insurers may reflect the cost effectiveness of
investing in an online
channel for insurers if they have employed more expensive
traditional distribution
methods. By contrast, an increase in transaction volume after
establishing an online
channel was experienced by as many larger insurers as small
insurers. Finally, sur-
vey evidence that the establishment of an online channel erodes
current offline busi-
ness is not overwhelming. While only one in six large insurers
experienced an ero-
sive impact, as many small insurers as not experienced an
erosive impact. Whether
erosion is clearly attributable to the establishment of an online
channel is multi-
faceted and is likely dependent on a number of market factors
not addressed in this
research.

Taken together, the results in Tables 3 and 4 suggest that
insurance management has ex-
perienced increased business activity by adopting an online
channel that has not simply
been treated as a substitute for traditional business activity. The
economic experience
is less clear and depends on the marginal profitability of the
online customer vis-à-vis
more traditional methods.
FINDINGS: TECHNOLOGY WITHIN OPERATIONAL AREAS
An online channel and questions about its effectiveness is a
major strategic interest
of insurer management at a time when innovation pervades a
variety of day-to-day
operational activities. The opportunity to move away from a
bricks and mortar office
environment to a “virtual office” presents the possibility of
significant cost savings
for insurers; however, it introduces concerns about employee
productivity, effective
communication, and the value of an office work-setting Fritz et
al. (1998) examine
satisfaction among telecommuters vis-à-vis nontelecommuters
and find telecommuters
reported higher communication satisfaction, potentially
alleviating upper management
concerns about introducing a virtual workplace environment. In
our survey we were
interested in how insurers viewed the virtual office concept. We
found that only about

half of our full sample of survey respondents indicated that the
virtual office concept is
or has been important to their business strategy. Only a little
more than 36 percent of
small insurers have embraced the virtual office concept, while
about 67 percent of large
insurers have done so.
While the virtual office and the opportunities inherent in the
Internet and online services
have a history, albeit relatively brief, a primary focus of this
research is to report on
management’s view of technologies that are being driven in the
current marketplace. We
broke down the survey by asking participants to focus on three
key insurance business
processes that define an insurer and separate it from other
financial services firms. While
many insurers, particularly large insurers, would be more aptly
described as offering a
menu of financial services and products, some of which relate to
insurance, we limited
our set of questions to those related to traditional insurance
functions: marketing-,
underwriting-, and claims-related technology.
Marketing
Our interest in the technological impact on marketing begins
with how insurers view
the development of a website as a way to market directly to
consumers. Insurers were
asked to assess this question by choosing a range of choices
from “not significant at
my company” to “very large impact on value of our company.”
In reporting the results
we identified a small insurer by the smaller arrow ( ) and large

insurers by the
larger arrow ( ).8 The impact varies considerably by size of
insurer. Large insurers
recognize that such development has been measurable while
small insurers have not
8 Average differences that are statistically significant are noted.
Otherwise, average differences
among large versus small insurers were not found to be
statistically significant. Underlying
survey data were organized in Excel. An excellent source for
statistical testing using Excel can
be found at http://cameron.econ.ucdavis.ed u/excel/excel.html.
In addition, the numbering of
reported results for marketing, underwriting, and claims
questions is consistent with the survey
instrument but are presented in the article based on the
compositional approach.
noticed a significant impact. No insurer in the sample responded
that a site dedicated to
direct selling to the customer has had a very large impact. By
contrast, insurers appear
to see more value in a website that, while focusing on the
customer, serves the purpose
of connecting the customer with an agent. Large insurers, on
average, see the impact to
be significant at their company, and small insurers recognize
the impact tending toward
at least measurable and noticeable. Thus, while the Internet is
perceived to convey

marketing benefits to insurers, agents have not been replaced by
this technological
innovation.
M2: Development of retail website focused on direct marketing
to consumer∗
Please choose only one of the following:
Not significant at my company 1
At least measurable and noticeable 2
Moderate, but not very large 3
Significant at my company 4
Very large impact on value of our company 5
*statistically significant at the 0.05 level
2.50
1.36
M3: Development of retail website focused on consumer but
connecting consumer
with agent∗

3.83
1.91
Given agents remain important to the sales process, how does
technology play a role
in insurers training their agents? We asked respondents to
assess three different forms
of training delivery methods with their agents: webcasts,
podcasts, and personal dig-
ital assistants (PDAs). Oloruntoba (2006) defines and discusses
a variety of current
learning technologies. Webcasts represent “streaming vi deo
delivered online.” Pod-
casts and PDAs have the ability of being more mobile, and the
functionality of a per-
sonal digital assistant taking the form of a “smartphone,” which
is a “hybrid mobile
phone/personal digital assistant.” By contrast, a podcast “is a
method of distributing
multimedia files . . . using atom syndication formats for
playbacks on mobile devices like
i-pods and personal computers.”
M4: Training agents through webcasts∗

3.50
1.91
M5: Training agents through podcasts
1.5
1.09
M6: Training agents through PDAs

1.33
1
In our survey, the findings are clear that PDAs do not have a
significant impact on
how insurers train their agents. However, webcasts are
important and among large
insurers approach being significant sources of training on
average. Podcasts are clearly
not significant at small insurers and only slightly less
insignificant at large insurers. Part
of this explanation may reside in the fact that while a key
strength of podcast technology
is as a more mobile training tool compared to a streaming
webcast, a mobile device is
required to take advantage of the technology.
M7: Communication of any kind with agents using mobile data
devices

1.83
1.81
The evidence is clear that marketing training via a handheld
device does not get the
attention of the insurance industry. We asked insurers to assess
whether they had any
communication at all with agents via a mobile data device, and
the average response
indicated that such communication was only approaching being
noticeable and mea-
surable.9 Thus, the marketing impact of technology appears to
be focused on linking
customers with agents, a slow movement by the industry toward
web-based agent
training and little need or perceived value in communicating
with agents via mobile
devices.
Underwriting
We are next interested in how insurers use technology to gather
information necessary
to evaluate the risk profile of their insurance applicants. In an

underwriting context,
information feeds knowledge and offers an insurer the
opportunity to (1) gain compar-
ative advantage over their competition and (2) narrow the
asymmetry that often exists
when insurance applicants know more about their intrinsic
risk.10
Technology can serve a variety of points in the underwriting
process so we were first
interested in whether insurers acquired their information from
agent input on a website
or internal network, and whether customer applicants were
permitted to self-input at
least some of their underwriting information directly. The
results indicate that agent
web-based input of data has become adopted, on average, even
among smaller insurers,
and that 4 of 17 insurers responded that web-based input had a
very large impact on the
value of their company. Smaller insurers appeared to emphasize
the web over an internal
network on average compared to their larger counterparts. By
contrast, customer entry
of underwriting data has not gathered much traction at small
insurers but has become
at least noticeable among large insurers.
U1: Permit customer entry of some underwriting information via
a website∗

2.67
1.36
9 While we have attempted to maintain consistency in the
numbering of this section M1, M2,
etc. between these tables and the actual survey, you will note
that the survey instrument has
7 questions for the section on marketing. Survey questions M1
and M7 were duplicates. We
omitted M1 when reporting results and kept M7. Survey
respondents did vary in how they
answered M1 and M7. M1 in the survey had a mean of 2.67 for
large insurers and 1.90 for small
insurers.
10 Readers are encouraged to read Deborah Smallwood’s piece
on how underwrit-
ing is changing,
http://www.fairisaac.com/NR/rdonlyres/F1DFEA70-14D4-
4A3E-A1EB-
76B4DA78943B/0/Competitive_PandC_Underwriting_Tower_G
roup_Oct_2004.pdf

U3: Permit agent entry of underwriting information via a
website
3.16
3.72
U4: Permit agent entry of underwriting information via an
internal network
3.33
2.63

Once the data are received by an insurer, how are they
evaluated? The utilization of
an expert system to evaluate the underwriting data is somewhat
dependent on the
market niche of the insurer; for example, commercial
underwriting is often more subject
to human judgment. We were interested how a respondent
viewed and implemented
an expert system in their underwriting process, and the average
result across the full
sample indicated that such a technology had at least a moderate
impact. The result for
large insurers tended toward a significant impact at these
companies, likely reflecting
that major automobile insurers were included in this sample.
U2: Implemented the use an underwriting expert system
3.67
2.73
More specific technologies for gathering customer information

include GPS mapping
of property exposures, mileage monitors in automobiles, and
whether insurers encour-
age and rely upon their customer’s self-reporting of mileage.
Both GPS mapping and
mileage monitoring begin to encroach on the world of
Telematics, where the com-
bination of global positioning systems with wireless
communication create a real-time,
individual-specific set of metrics that can be evaluated by
management to assess whether
these risk-attributing metrics are correlated with actual
losses.11 In effect, automobiles
are outfitted with devices that gather and communicate driving
data. If a driver uses
their car to drive “to and from work,” the ability to gather and
communicate real-time
data would permit, say, the time of day when driving is
occurring, the average speed
at which the car is moving, and the quantity of miles driven by
day of the week. The
number of new attributes that could be created for underwriting
and pricing evaluation
would be limited only by the creativity of management, and
statistical evidence from
the modelers that such attributes are valid. A key value
proposition being the additional
precision obtained in predicting future losses.
In our survey we focused on current practices of some insurers

within the industry.
The average insurer response to GPS mapping technology to
help gauge property in-
surance exposure is having at least a noticeable effect, while
mileage monitors in au-
tomobiles is not yet a significant practice among insurers.
While insurers can utilize
GPS mapping to help assess catastrophic exposure that can be
used for “big picture”
management decisions, outfitting customer’s automobiles with
devices is both costly
and potentially perceived by the customer as privacy invading.
Indeed, one of the issues
raised by Holdredge (2005) and echoed by the insurance
industry is whether privacy
concerns will outweigh the actuarial justification for a more
focused measure of loss-
producing capability. One way around privacy concerns is self-
reporting and our survey
asked whether insurers might encourage Internet facilitated self-
reporting of mileage
by their insured customers. The results were not surprising
given the degree of diffi-
culty for insurers to independently verify individual mileage
assessment ex ante loss.
Sixteen of 17 insurers answered that such self-reporting was not
significant at their
company.
To round out the exploration of underwriting we were interested
in whether under-
writers were communicating with any other insurer party
through the use of a mobile
data device. While smaller insurers appeared more inclined to
utilize this technology in
contrast to large insurers, the overall results tend toward this

communication path not
being very noticeable among insurance industry participants.
U5: GPS mapping of property exposures
2.66
2.36
11 Holdredge (2005) provides an insightful discussion on the
basics of Telematics and how it can
provide an insurer with strategic advantage.
U6: Utilize mileage monitors in cars

1.16
1.09
U7: Utilize self-reporting of mileage by consumers via a
website
1.09
1.00
U8: Communication of any kind with underwriting and another
party using mobile
data devices

1.66
1.36
Claims
Once marketing and underwriting turn a potential risk/business
opportunity into a
customer, the prospect of claims inevitably becomes the
subsequent consideration. Ef-
fective claims management recycles information back to
underwriters and actuaries
when claims are handled in a timely and accurate manner. This
integration of activities
possesses much value-added potential. Within claims the
“optimization proposition”
is for insurers to accurately assess its true claim contractual
obligation then pay the
obligation in the appropriate time frame subject to customer
satisfaction.
Historically, claims management has relied on a manual
assessment of claim validity
with quality reviews undertaken by manual review, too. Perhaps
more than any of the
other functional areas of an insurer, claims management has
moved farther down the
path of adopting technological innovation.12 There is now the
opportunity for insurers

12 I am grateful to the insights of David Repinski, president of
Cunningham & Lindsay, N.A., for
much of the background to this section.
to not only effectively handle claims in real time but to gather
information from such
claims to make better future decisions.
Today, the process of adjusting a claim begins with an 800
number and insurers have
the opportunity to handle claims and validate the accuracy of
claim payments utilizing
mobile hardware with a software interface that is provided by
firms such as Marshall,
Swift, and Boeck (MSB) and Xactimate. These firms work as an
electronic intermediary
between the insurer and the adjuster. Once notified of an
incident, the insurer uploads
claim opening data to the claim vendor’s site and gives the field
adjuster access. The field
adjuster downloads the basic facts of a claim, investigates the
claim, and then uploads
the results of the claim investigation back to the claim vendor’s
site. An advantage of
electronic communication is that it permits the stocking of a
data warehouse that can
be mined for claim-handling assessments, quality testing, and
adjuster performance
review.
In our survey we were interested in how insurers utilize

technology to process in-
dividual claims. The questions took respondents through the
process beginning with
whether they communicated with field adjusters via a cell phone
when the claim was
incurred. Large insurers, on average, reported that mode to be
significant at their com-
pany while small insurers reported a more moderate response at
their companies. Once
the claim process had begun, we were interested in whether
adjusters utilized digital
photography and digital recorders to supplement a claim file.
Overall results indi-
cated that digital photography to be a significant and valuable
resource to the insurer
respondents. While digital recorders in the field document
aspects of the event and
thus play an important role for insurers, the overall average
result was only mod-
erate. The use of portable printers in the field to give customers
an on-site estimate
yielded a comparable result to digital recorders overall and
clearly plays a more im-
portant role among large insurer respondents that view portable
printers as significant
at their company. An obvious need for insurers that is enabled
by technology is quick
communication between the field and the home office. We
wanted to know about the
role of wirelessly enabled laptops that permit adjusters to
update an electronic file. We
found that to be an important trend among all insurers, with the
average result falling
between a “moderate” impact at their company and a
“significant” impact at their
company.

C1: Communicate with adjuster via cell phone when notified of
a new incurred
claim∗
4
2.72
C2: Digital photography included in claim file to help
assessment

3.73
4.5
C3: Use of laptops with wireless technology so field reps can
update electronic file∗
4.67
3
C6: Use of portable printers on site so customer receives
estimate in hand∗

3.67
2.18
C7: Use of digital recorders to take a statement in the field
3.5
2.55
The use of external software providers to help process claims
electronically is more in
favor compared with internally developed methods. Nine of 17
respondents reported
that internally developed software was not significant at their
company, while overall
average results indicate that internally developed methods were
reported to be some-
what more than “not significant.” By contrast, the average
overall value for this choice
was substantially below external products such as MSB and
Xactimate that have had at

least a moderate impact on the value of the insurer respondents.
C4: Use of MSB, Xasctimate or other external vendor software
in measuring value of
property claim
3.67
2.81
C5: Use of internally developed software to measure value of
property claim
2
1.63
How has technology permitted the centralization of call centers?
The results from our
sample indicate that centralization has had a significant impact
on insurer value, par-
ticularly among large insurers for whom centralization is

revealed as an important
consideration. Even among our defined sample of “smaller”
insurers we found that
centralization has had a moderate impact on insurer value.
Finally, we were interested
about the role and use of a high technology vehicle in managing
claims and communi-
cating via satellite technology. Not surprisingly, this technology
has been embraced by
larger insurers that have a focus on personal lines, and the
overall impact on company
value is slightly above the moderate level. Among smaller
insurers the impact is nearly
measurable and noticeable. The use of field-based global
positioning systems to help
locate an insurer’s customers does not play a significant role at
smaller insurers with 7 of
11 respondents indicated that these devices are not significant at
their company. Larger
insurers rate the impact as only moderate.
C8: Centralization of call centers∗
4.67
2.91

C9: In catastrophes use of a high-technology vehicle that
communicates via satellite
technology∗
3.33
1.81
C10: Use of portable global positioning systems when difficult
to locate damaged
property∗

3
1.81
Integration
As we have seen thus far, not all aspects of the insurance
industry’s operational
makeup is technology enabled, and the extent to which an
insurer’s processes are
digitized and connected depends on size. We expect that
economic gains from uti-
lizing technology in an information-driven business can be
substantial and enhanced
if the technology is integrated across these processes. We
inquired among survey re-
spondents if and how they integrate customer data across their
property and liability
lines of business by asking respondents to choose one category
that best describes their
insurer.
The results in Table 5 show that nearly 65 percent of insurer
respondents do inte-
grate their customer data across marketing, underwriting and
claims. An additional
23.5 percent of insurers expect to accomplish this task within 3
years. Whether an in-
surer is small or large, integration appears to be both key and
workable. Since many
insurers are multiline we were curious about the ability of
insurers to integrate their
customer data on the property and liability (P&L) side to the
life and health (L&H) side
of their businesses. Only 29 percent of our respondents had an
L&H presence. Among
these multiline insurers, none of them have a P&L system that

“easily interfaces” with
their L&H system.
TABLE 5
Integration of Customer Data
Percentage
response
We presently find it difficult to integrate customer data across
functional
areas
5.8%
We presently find it difficult to integrate customer data across
functional
areas but plan to do so within the next 3 years
23.5%
We presently integrate customer data across marketing and
underwriting,
but not claims
5.8%

We presently integrate customer data across underwriting,
claims, and
marketing
64.7%
The likelihood of moving toward complete integration of data
through businesses pro-
cesses could be enhanced because of the economics of service -
oriented architecture or
SOA. As defined by He (2003), SOA “provides for a loose
coupling among interacting
software agents.” By contrast, the practice of insurers evident
today is “still focused
on buying point solutions at the LOB [line of business] level”
(Gorman and Macauley,
2007). The distinction drawn between SOA and alternative IT
solutions is metaphorically
explained by He,
Take a CD for instance. If you want to play it, you put your CD
into a CD player
and the player plays it for you. The CD player offers a CD
playing service. Which
is nice because you can replace one CD player with another.
You can play the same
CD on a portable player or on your expensive stereo. They both
offer the same CD
playing service, but the quality of service is different. The idea
of SOA departs sig-
nificantly from that of object oriented programming, which
strongly suggests that
you should bind data and its processing together. So, in object
oriented program-
ming style, every CD would come with its own player and they

are not supposed
to be separated. This sounds odd, but it’s the way we have built
many software
systems.
One of the compelling features of SOA is in its flexibility it
captures complexities that en-
able insurer management to enhance the value of information
while lowering processing
costs. We asked survey participants their views about how they
view SOA contributing to
their management strategy. We found the prospect of
widespread SOA adoption persua-
sive, as nearly 73 percent of small insurers and 67 percent of
large insurers have SOA as ei-
ther a current or planned approach to their technology
infrastructure. During the survey,
insurers were given the chance to offer their view about the
benefits of SOA to their firm.
One respondent noted that SOA would “foster the environment
needed to effectively
create, utilize, promote, and support reusable artifacts (i.e., use
cases, process maps, data
models, patterns, software components, test cases, etc.), and to
provide centralized sup-
port for communications.” Similarly, one insurer noted that “w e
expect to have business
logic and functionality written once and maintained in single
software modules for ease
of maintenance and reuse.” While one insurer summarized SOA
advantages in terms

of cost reduction, noting that their company would be able to
have “efficiency gains
in staffing” and that now there would be a “single portal for all
agency and consumer
transactions,” two other insurers expressed a top line impact,
noting that speed to market
would increase when changes have to be made quickly and that
SOA permitted “rapid
deployment of new applications.” One notable barrier to the
widespread adoption of
SOA, as noted by Gorman and Macauley (2007), is the tension
the exists between a lack of
standards or comparability in data and technology from one
insurer to the next, and the
economies necessary for third-party vendors to come up with
effective and creative SOA
applications.
CONCLUDING REMARKS
This research has reported on how technology is currently
shaping business prac-
tice in the insurance industry by examining a number of
innovations related to tra-
ditional insurance operational areas from a set of 17
respondents that, while lim-
iting in number, included several of the largest insurers in the
United States mar-
ket. When respondents were presented with a broader, senior-
level query to cite
those implementations of technology that have had a significant
impact on company
value over the past 10 years, we learned that electronic
communication of business
processes such as agent and consumer portals and the paperless
office have been
key.

While the findings also reveal more recent pervasive technology
utility within mar-
keting, underwriting, and claims, a significant finding is the
extent to which insurers
are embracing integration and use of customer data across their
traditional practice ar-
eas, which is facilitated by technology advances coupled with
the prospects for SOA.
The internal synergy helps management create an information
currency that brings
new aspects of business within their control for evaluation and
strategy. How man-
agement effectively utilizes today’s technology to streamline
existing processes is one
of the value increasing opportunities that will separate winners
from losers in the
future.
APPENDIX

REFERENCES
Bagby, W. S., 1957, Automation in Insurance, Journal of Risk
and Insurance, 24: 158-167.
Berger, A. M., M. A. Weiss, and J. D. Cummins, 1997, The
Coexistence of Multiple
Distribution Systems for Financial Services: The Case for
Property-Liability Insurance,
Journal of Business, 70: 515-546.
Bair, S. L. et al., 2004, Consumer Ramifications of an Optional
Federal
Charter for Life Insurers. World Wide Web:
http://www.isenberg.umass.edu/
finopmgt/uploads/textWidget/2494.00004/documents/bair -cons-
ramifications.pdf.
Campbell, D., 2003, The Cost Structure and Customer
Profitability Implications of Elec-
tronic Distribution Channels: Evidence From Online Banking,
Working Paper, Harvard
Business School.
Cummins, J. D., and J. VanDerhei, 1979, A Note on the Relative
Efficiency of Property–
Liability Insurance Distribution Systems, Bell Journal of
Economics, 10: 709-719.
England, C., 2005, Federal Insurance Chartering: The Devil’s in
the Details. World Wide
Web: http://cei.org/pdf/4358.pdf.
Fritz, M. B. W., S. Narasimhan, and H.-S. Rhee, 1998,

Communication and Coordination
in the Virtual Office, Journal of Management Information
Systems, 14: 7-28.
Gorman, M., and M. Macauley, May 2007, Service-Oriented
Architecture: Hope or Hype
for the Insurance Market, TowerGroup.
Grace, M., and R. Klein, 2000, American Enterprise Institute.
Harrington, S. E., 2002, Alliance of American Insurers.
He, H., What Is Service-Oriented Architecture? World Wide
Web: http://webservices.
xml.com/pub/a/ws/2003/09/30/soa.html.
Holdredge, W. D. 2005, Gaining Position With Technology,
Emphasis, 2-5.
Myhr, A. E., and J. J. Markham, Insurance Operations,
Regulation, and Statutory Ac-
counting, American Institute for CPCU/Insurance Institute of
America, Malvern, PA.
Oloruntoba, R., 2006, Mobile Learning Environments: A
Conceptual Overview. World
Wide Web:
https://olt.qut.edu.au/udf/OLT2006/gen/static/papers/Oloruntob
a_
OLT2006_paper.pdf
Pritchett, S. T., J. T. Schmit, H. I. Doerpinghaus, and J. T.

Athearn, 1996, Risk Management
and Insurance (Eagan, MN: West Publishing).
Skipper, H. D., and W. J. Kwon, 2007, Risk Management and
Insurance: Perspectives in a
Global Economy (Malden, MA: Blackwell Publishing).
Stoll, B., and K. Cullen, 2005, Elevate Claim Performance via
Technology, Emphasis,
18-21.
Trenerry, C. F., 1926, The Origin and Early History of
Insurance (P. S. King & Son, Ltd.).
Trieschmann, J. S., R. E. Hoyt, and D. W. Sommer, 2005, Risk
Management and Insurance
(Mason, OH: Thomson South-Western Publishing).
Copyright of Risk Management & Insurance Review is the
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to a listserv without the copyright holder's
express written permission. However, users may print,
download, or email articles for individual use.
The assessment is designed around the core question for this
course- ‘how do we deliver value to our customers in a
sustainable manner?’ Or even more simply ‘how do we make
truly sustainable products?’ The report is a basically a
marketing plan but one uses frameworks learned on the course
(Circular Economy, NSF, Input-Output analysis) to ensure that

what is proposed is moving the product to a point in the near
future, where you can say with confidence, that it is sustainable.
Be careful when choosing a product (I am using the broad
definition of product here- a physical good, a service or an
idea). You will need to be able to find out information on what
it is made of and its supply chain, so avoid complex goods and
services. I cannot expect you to find out exact details of the
materials used and where they come from but you should be
able to find out about similar or approximate materials and
processes. For example, I can’t expect you to find out the exact
process by which Zara make a wool/nylon blend of fabric and
where exactly these come from. But you can find out where and
how nylon is made and the modern supply chain for wool. You
can build your case from here on what you would change,
though you should mention and appraise the limitations of your
data.
You have to write the assignment by answering the part 1
questions bellow :
Part 1: The Current Product and Sustainability Diagnosis
1. Executive Summary*
1. Short overview of key findings. NOT a section that
just tells me about what you did. Tell me what you found and
plan to do.
2. Company and Brand Introduction
1. A brief overview of the company, its capabilities and
the product
3. Table of Contents*
4. Situational Analysis
1. Macro-environmental analysis
2. Micro-environment analysis
3. SWOT
5. Market and Marketing Summary:
1. Segments, Targets, Current Position
2. Statement of Value (Market needs being satisfied) and
Customer benefits.

6. Marketing Mix (4 or 7 depending on product chosen)
Examine the current mix to set out how value is created,
communicated and delivered whilst using sustainability
frameworks to highlight issues that need t be addressed. For
example, the Product section would include a review of what it
is made of, packaging, labelling, product range) AND an
analysis using sustainability framework to highlight for
example, an LCA or Input-Output based assessment of the
impact of the product and issues diagnosed using the NSF and
CE.
1. Product (Idea, good, service)
2. Price (Including cost to Citizen Consumer)
3. Distribution (Channels)
4. Promotion (Communication)
5. Process
6. People
7. Physical Evidence
7. Summary of Key sustainability and broader value based
issues
i.e. what issues must changes to product address, ‘broader
value’ means the output of the ‘Understanding value phase
highlight changes required to Creating, Communicating and
Delivering value
2, 299-309
DOI: 10.1111/j.1540-6296.2011.01200.x
EDUCATIONAL INSIGHTS
USING TECHNOLOGY TO ENCOURAGE CRITICAL
THINKING
AND OPTIMAL DECISION MAKING IN RISK

MANAGEMENT EDUCATION
John Garvey
Patrick Buckley
ABSTRACT
This article draws a link between the risk management failures
in the financial
services industry and the educational philosophy and teaching
constraints at
business schools. An innovative application of prediction
market technology
within business education is proposed as a method that can be
used to encourage
students to think about risk in an open and flexible way. This
article explains
how prediction markets also provide students with the necessary
experience to
critically evaluate and stress-test quantitative risk modeling
techniques later in
their academic and professional careers.
INTRODUCTION
The financial and economic crisis that we continue to endure
presents a serious challenge
to the teaching and learning strategies employed in universities.
Business graduates are
expected to have a deep knowledge of the theory that forms the
bedrock of the financial
system as well as the mathematical competence necessary to
apply asset pricing and risk
management methodologies. However, the techniques and
models used to control and
manage risk are often taught in an environment that does not
provide sufficient space
and time for rigorous debate and critical analysis.

Students are often presented with subject knowledge in a way
that the content has al-
ready been carefully selected and sequenced by their lecturer.
The education literature
already notes that this method of providing teaching materials
prevents an active learn-
ing dynamic (Kinchin, Chadha, and Kokotailo, 2008). In the
early stages of university
business programs, the often large class sizes limit the
opportunity for students to engage
in realistic decision-making scenarios. The project described in
this article is founded on
providing students with an early testing ground for the
application of risk management
theory. The creation of a closed market populated by other class
members is a departure
from the traditional approach where students learn about the use
of statistical mea-
sures of risk such as standard deviation and correlation and
become familiar with their
John Garvey is a Lecturer in Risk Management and Insurance,
Kemmy Business School, Uni-
versity of Limerick; e-mail: [email protected] Patrick Buckley
is at the Kemmy Business School,
University of Limerick; e-mail: [email protected]
299
practical relevance to industry standards such as beta or value -
at-risk through lectures
and formulaic practice. The application by students of statistical
methods in a real-time

insurance market demonstrates the relevance of human behavior
and expectations in
driving market dynamics.
Beyond the confines of the university campus we can observe
increasing pressure on the
insurance system to underwrite risks previously considered
uninsurable. The insurance
system is absorbing potential claims associated with
catastrophic risks posed by natural
hazards such as earthquakes and windstorms and in some cases
man-made hazards
associated with technologies as nuclear, biological, and
chemical engineering. This trend
is occurring at a time when the industry is beset by narrower
profits as large volumes of
capital compete for a limited range of risks. There is now a
large category of insured risks
that are being priced and underwritten using techniques that do
not apply the age-old
mathematical comforts of the law of large numbers and the
central limit theorem.
This article describes an innovative teaching mechanism that
has been applied to a
large group of undergraduate students at the Kemmy Business
School, University of
Limerick. We document how the teaching and learning
environment has been dramati-
cally changed through the introduction of a prediction market
where students estimate
and transfer insurance risks. The market structure encourages
students to think about
risk outside the confines of the lecture theatre. The competitive
nature of the mar-
ket and the sparse historical information that is made available

require students to
explore the strengths and limitations of traditional risk
management techniques. Impor-
tantly, the students’ participation in this dynamic and complex
environment coincides
with their introduction to formal ways of thinking about risk
management. Because
of this, the market activity provides a reference point during
lectures so that students
engage in dialogue and listen in an open and flexible way. The
dynamic nature of the
market and its direct and timely link with the course content
encourages students to
learn at a “deep” level. It provides them with skills that they
can bring to bear in the
learning process outside of the specifics of this module.
In this article, we document the prediction market structure as it
is used in an under-
graduate risk management module taken by 430 undergraduate
students. The module
is an introduction to a specialty stream in risk management and
insurance. Graduates
in this specialty go on to work in roles as varied as risk
analysis, insurance and rein-
surance underwriting, and fund management. These roles
primarily require an ability
to accurately identify and assess risks using historical data in a
variety of quantitative
risk models. In practice, risk decision making is also influenced
by the existing risk
profile of the organization, the requirements of regulators, as
well as pressures relating
to performance. The many technical skills required in risk
decision making must often
be applied with subjective elements of judgment. The prediction

market allows students
to observe the reflexive nature of their decisions in a dynamic
environment.
The article is structured as follows. The Introduction section
introduces the motivation
for the current study. “Risk, Insurability and Education”
provides a context for the
use of prediction markets in risk management education by
focusing on the challenges
faced by the insurance industry and the changing nature of
insurability. “The Insurance
Loss Market” discusses the importance of class interaction and
critical thinking in the
context of education and risk management. This section also
describes the design of the
Insurance Loss Market. “Results on Risk Decision Making and
Learning” describes the
THINKING AND OPTIMAL DECISION MAKING 301
results of the research and examines the effectiveness of
prediction markets in engaging
students and augmenting learning outcomes. “Conclusions”
discusses the future of risk
management education and the development of innovative
techniques that inform risk
decision making.
RISK, INSURABILITY, AND EDUCATION
Within the education environment and business schools in
particular, the constraints of
time and demands from employers for practical and technical

knowledge leaves little
space for the exploration of how decisions are made in the
absence of known ex ante
probability distributions. Third-level education in risk
management focuses on how
practitioners undertake decisions when faced with ex ante
probability distributions that
are known. Graduates who specialize in risk management and
finance learn a great deal
about the quantitative and technical aspects of risk decision
making. Popular, quanti-
tative models, such as value-at-risk, are a generally
incorporated into taught modules
at both undergraduate and postgraduate levels. In this teaching
environment, Frank
Knight’s important distinction between risk and uncertainty is
rarely linked directly to
industry practice and is likely to be relegated to an historical
artifact (Knight, 1921).
The assumption that we can accurately estimate ex ante
probability distributions is the
foundation for many of the risk models used by the insurance
and banking industries
and interpreted by regulators. For academics, both as
researchers and as teachers there
is a recognition that effective business education should provide
students with the op-
portunity to actively apply and evaluate decision making in an
environment that closely
approximates real-world decisions. In this article, we show that
this can be achieved by
providing student’s with this opportunity early in an
undergraduate business program
before their perspective on risk is influenced by traditional
thinking and contemporary

risk models. As we can observe from the ongoing financial
crisis, of the set of risks that
are priced and managed within the financial system an
increasing proportion extend
beyond the limiting parameters required for models such as
value-at-risk. If they are to
become effective risk management professionals, it is important
that graduates become
aware of the Knightian uncertainty of the real world, rather than
imposing a strict mathe-
matical framework on their decisions. The management of
uncertainty can be achieved
much more effectively through conservatism and avoidance and
simple diversification
methods where possible.
There is a growing awareness that traditional teaching methods
in risk management
and finance are somewhat narrow. This awareness has grown
most acutely over the past
2 years as we have seen the near collapse of the banking system
and the failure of a
number of institutions. However, the failure in risk management
is the most recent and
devastating in a lineage that can be traced back through Enron,
LTCM, and Barings Bank.
These risk management failures have prompted a variety of
responses from corporations
and regulators. Within education, business graduates now have a
greater awareness of
the limitations of quantitative risk models and there has been a
general trend toward
including new subject areas such as governance and ethics for
those engaged in finance
and risk management. Although this trend is laudable in some
respects, a criticism of

this approach might be that graduates compartmentalize the
different subject areas, and
are unlikely to later draw on issues relating to governance and
ethics when they are
engaging in risk management.
Within business education a number of techniques have been
developed that allow stu-
dents the opportunity to apply their knowledge of relevant
theory in a realistic setting.
In risk management education the breadth of case study and
market applications is
proof of the need to sharpen traditional teaching techniques so
that university students
fully appreciate the challenges of risk management. Projects
using computer simula-
tion have been described by Hoyt, Powell, and Sommer (2007),
Born and Martin (2006),
and Joaquin (2007). Hoyt et al. introduce commercially
available software produced by
Riskmetrics to examine value-at-risk. Similarly, Born and
Martin simply adopted the
software provided by AIR Worldwide to allow students to apply
the software used
in catastrophe modeling. Joaquin describes the application of
spreadsheet-based sim-
ulation in loss modeling. While these approaches are effective
in allowing students to
practice and refine their skills, they are essentially static in
nature and as with many risk
models there are significant model assumptions made a priori.
The project described in

this article is also very different from the insurance market
simulation used by Russell
(2000). Rather than simulate an insurance market, we use
actual, real-time insurance
data and prediction market software. The activity of market
participants (in this case,
the students) creates the pricing dynamic by evaluating likely
insurance losses.
Other approaches in creating an insurance market type
environment generally take
the form of a case-study-type project that requires students to
recommend specific
business decisions. The application of classroom games is
described by Barth et al. (2004)
and Eckles and Halek (2007). The effect of risk framing on
choices under uncertainty
is explored in the games structured by Barth et al., while the
impact of asymmetric
information is a specific objective in the classroom games
structured by Eckles and
Halek. The dynamic environment created by an interactive
prediction market provides
a forum to undertake decisions and compete against peers that is
distinct from these
earlier projects. By using a prediction market and obtaining data
on an underlying
“asset,” in this case state-wide insurance industry losses, we are
not imposing decision
parameters on students. Instead, students evaluate and
reevaluate their decision-making
criteria and learn to appreciate the emotional and psychological
inputs into risk decision
making in a realistic setting.
THE INSURANCE LOSS MARKET

We describe here a prediction market structure as it is applied
in an undergraduate
business program at the University of Limerick. Prediction
markets are also known as
collective intelligence networks, and the software required for
their operation is available
from a number of commercial providers. Prediction market
platforms allow multiple
users to make forecasts about the probability of future events as
diverse as movie box
office sales and election results. By forecasting a specific
outcome, individual market
participants marginally influence the expected probability of
that outcome. With large
numbers of market participants accurate and reliable estimates
of event probabilities are
likely to emerge. The dynamic nature of the prediction market
allows these probabilities
to fluctuate in real time as participants act and react to the
arrival of new information.
The prediction market described here used software provided by
QMarkets, one of a
number of commercial providers. The increasing popularity of
prediction markets and
the greater breadth of applications have encouraged the creation
of open source software
that allows users to download and create their own prediction
markets. Thus, this type
of project could be easily replicated in other educational
settings.

We describe here the application of a prediction market that is
designed specifically for
an undergraduate module, called Principles of Risk
Management. This module intro-
duces students to the qualitative and quantitative skills required
in risk assessment, risk
control, and risk financing. The module is delivered in a
traditional format through a
series of lectures and tutorials that were offered over a 12- 1
week semester. The learning
outcomes identified are reinforced through student participation
in a custom-designed
prediction market called the Insurance Loss Market (ILM). This
market allows the
430 undergraduate students registered for the module to forecast
weekly losses in the in-
surance industry. Specifically, students are required to predict
weekly insured property
losses estimates for California, New York, and Florida.1 The
details of the forecasting
and trading process are detailed in the next section. The market
dynamic allows students
to activate their skills in mathematical competency and
qualitative risk assessment in
real time. During each 5-day period, each student was required
to undertake at least
one trade in each of the three states. The ILM was open for
trading 24 hours a day and
it was run over a 10-week period. At the market close on each
Friday, their forecasts
were evaluated against the gross property loss estimate as
notified by data provider,
Xactware. The simplicity of the ILM interface and data
provided by Xactware concealed
a sophisticated process that allowed for the provision of highly

accurate data at the end
of each week.2
Market Operation
At the beginning of every week, Monday 9 a.m., each student is
provided with
5,000 units in notional “risk” capital that they must allocate to
loss bands in each of
the three U.S. states. Figure 1 provides a screenshot of the ILM
interface. Historical data
on insurance losses for the three states are made available to the
students at the beginning
of the semester, and the first 2 weeks of the semester are used
to allow students to famil-
iarize themselves with the operation of the market. During this
period students learn
quickly about the variability in weekly insurance losses. Gi ven
the element of “luck” in
making an accurate prediction students were required to use a
number of aspects of risk
management so that their capital allocation strategy performed
consistently from week
to week. As discussed in the following section, the students who
performed consistently
1 The data providers, Xactware, included 5 years of loss data
for each of the three states. These
were made available to students at the beginning of the semester
and they were encouraged
to consult this data bank when undertaking decisions. Although
there was a degree of “luck”
attached to forecasting losses, the exercise demonstrated to
students how to apply historical
data could be useful, but had to be used with care. In addition,
the element of accuracy required
of the students was reduced by requiring them to forecast loss

bands rather than point estimates.
2 The process flow used by Xactware to generate the data can
be described as a “full-cycle
claims workflow.” Each week, Xactware typically receives a
first notice of loss from an insurer
that includes the type of loss, the physical address of the loss
location, along with varying
amounts of supporting information dealing with coverage
types/amounts, and a description
of the circumstances surrounding the loss. This information is
then forwarded to either a
claims adjuster, repair contractor, independent adjuster or
someone else who is responsible for
completing an estimate of repairs. That recipient connects to the
Xactware network, using a
local installation of their estimating application (Xactimate),
and proceeds to complete a unit
cost repair estimate of the damages. Once completed, the
recipient uploads the final estimate to
the network (XactAnalysis) where Xactware mine the various
data elements contained in that
detailed repair estimate.
FIGURE 1
ILM Screenshot
Note: The New York market is shown. Trading activity by
market participants implies that there
is a 10.6 percent probability that losses in New York will be
>$9m and < = $10m for the week
ending October 9, 2009.

well are those who recognize that the “luck” element can be
reduced through allocating
capital across a number of loss bands in each state.
As trading activity commences the market dynamic will produce
an expected distribu-
tion of likely outcomes as participants evaluate historical
information, such as recent
weather patterns, insurance hazards and loss statistics as well as
forward-looking infor-
mation such as hurricane development, weather forecasts, and
potential hazards such as
wildfires posed by prolonged period of data. There is wide
availability of new informa-
tion on weather-related hazards such as fires, windstorms, and
hail as well as other rele-
vant information. Market participants must evaluate the
importance of the available his-
torical information as well as the relevance of new information
when making a decision.
As participants select a specific loss band, its value increases
and simultaneously the
value of all other loss bands will decrease proportionately. In
order to increase trad-
ing and improve liquidity, most prediction markets use an
automated market maker.
When a buyer or a seller posts an order, the automated market
maker automatically
fills the order and adjusts the price of the asset using a
mathematical formula. In this
case, it is not necessary to match buyers and sellers. By
allowing transactions to occur
immediately it reduces the complexity of the market interface,
which has the effect of

lowering knowledge barriers and promoting participation
(Christiansen, 2007). Detailed
descriptions of the operation of automated market markers are
given by Hanson (2007)
who describes the market scoring rule and Pennock (2004) who
describes the dynamic
parimutuel market maker.
In a similar manner to assets traded on a liquid market, the
value of units in a specific loss
band may make it prohibitive, thus forcing students to make
alternative selections or
wait for the unit value of a loss band to fall. Many aspects of
market activity are similar
to that carried out in the insurance markets each day as
insurance and reinsurance
underwriters allocate, trade, and transfer insurance risks.
Importantly, participants in the ILM are predicting events in
“real time.” This overcomes
many of the weaknesses of alternative risk-decision
methodologies used in education
and industry, such as simulations using an historical event or
historical asset behavior.
The type and level of activity in the market is at the discretion
of each participant and
the decisions they make in this regard are seen as key part of
the learning process. All
decisions are taken on an individual basis; however,
consultation with classmates is
encouraged. In order to retain participation throughout the

semester, ILM participants
must undertake one trade in each state each week. There is no
upper limit on the
number of trades they can undertake and they can continue to
trade as often as they
like (“buying” or “selling” risks) throughout the week until the
ILM closes on Friday at
17:00. There are no transactions costs imposed on student
portfolios. Later that day or
early the following week the actual loss estimates for that
trading period are received
from Xactware. The closing position of each participant is
reconciled against the actual
loss data and is used to estimate the value of each student’s
portfolio, as shown in
Equation (1).
PortfolioA= Cash Balance + (UnitsCA × 100) + (UnitsFL × 100)
+ (UnitsNY × 100). (1)
The portfolio value for Participant A is calculated as the
number of units they hold in
the correct loss band for each U.S. state multiplied by 100 (100
percent) plus the cash
they did not allocate. The metric for evaluating activity and
decision making in the ILM
places primary importance on the forecasting accuracy.
RESULTS ON RISK DECISION MAKING AND LEARNING
The primary objective of this research is to create a challenging
learning environment for
risk management students. This environment should encourage a
more critical perspec-
tive on risk decision making and the popular quantitative
techniques that are applied in
practice. One of the interesting aspects of using the prediction

market was the immediate
change in mindset that it produced among the students taking
the module in Principles
of Risk Management. As mentioned, the ILM was live for a 10-
week period during the
fall semester 2009. This was preceded by 1 week in which
students were encouraged to
access the ILM for a trial period of 1 week. The simplicity of
the questions and the nature
of the underlying risks being evaluated facilitated immediate
participation by a large
proportion of the class. During the initial weeks of the semester
very few instructions
were provided to participants.
The minimal level of guidance provided during this initial phase
was deliberate and
it had the desired effect of creating discomfort among
participants as they attempted
to evaluate the possible range of gross property losses in New
York, Florida, and Cal-
ifornia during that week. This “hands-off” approach allowed
participants to evaluate
the decisions they were making in an unbounded atmosphere,
with little consideration
for the norms recommended by risk management theory and
practice. This approach
gave rise to informal queries from students during the trial
week, such as: “What is
the right approach?,” “When should I decide on the appropriate
loss band?” as well as

other comments that included, “Isn’t this just gambling” and “It
is hard to get enough
data to make a decision.” Decision making (trading) in the
market is motivated both
by fluctuating values in a specific loss bands as it increased or
decreased in popularity
and also through relevant external risk information provided by
sources such as the
National Hurricane Center.
Assessment for the module was designed to promote a high
level of participation
in the ILM structure.3 The level of activity in the ILM is also
revealed in Figure 3,
which summarizes the average number of trades undertaken in
California, Florida, and
New York. We can observe that in the initial week, there were
13.12 trades undertaken
by students in the California market, 12.74 in the Florida
market, and 10.11 in the New
York market. In the 10-week period, the average number of
trades undertaken showed
a marginal decrease. In the final week of the market the average
number of trades for
California was 7.61, and for Florida and New York trades
undertaken averaged 6.29 and
9.22, respectively. It is worth noting that, throughout the entire
10 weeks, participation
in the market exceeded the minimum participation limits that
were set as part of the
module requirement.
Following the first week of live trading in the ILM, participants
were provided with
historical data that gave gross property losses for each of the
three states for the 5-year

period 2004 to 2008.4 The provision of this information
coincided with the beginning
of a series of lectures on risk assessment and risk measurement.
These lectures intro-
duced students to fundamental concepts such as randomness and
variability around an
expected value as well as the useful characteristics of
normality.
Students were encouraged to examine the historical loss data
and explore how it could
be used in their ILM decisions. An experienced risk
management professional would
immediately recognize that the historical data would provide
only very crude predictive
information. For those participating in the ILM, the recognition
that historical data
must be used carefully was learned though the interactive
experience of evaluating and
undertaking and reversing decisions.
As the weeks progressed and students became more familiar
with the dynamic of the
ILM we reduced the width of the loss bands.5 From the fifth
week of live trading
on the ILM loss bands were held constant. This allowed us to
evaluate progress in
participants’ ability to undertake decisions and control their risk
exposure. A comparison
3 Twenty-five percent of the total marks in Principles of Risk
Management were assigned to ILM
part of the module. Marks were assigned on a weekly basis with
a total of 8 marks available
for participation (minimum of 1 transaction in each insurance
region), 9 marks for performance

relative to peers (Maxiumum of 9 marks (top 20 percent finish,
relative to peers) and declining
by 1 mark for 10 percent bands), and a maximum of 8 marks
available for a one-page report on
students’ decision-making behavior in the ILM.
4 Data provided by Xactware for quarterly (3-month) periods.
5 Changes to loss bands were initiated in California in Week 4
where bands were reduced from
$5m (e.g., losses will be > = $10 million and < $15 million) to
$1m (e.g., losses will be > = $10
million and < $11 million). Narrower bands were applied to all
states by Week 5 and remained
narrow for the remaining 5 weeks of live trading.
FIGURE 2
Weekly Trading by Region on the Insurance Loss Market
FIGURE 3
Weekly Data on the Number of Positions Held by Market
Participants
Note: The number of participants categorized with low -level
diversification fell, while participants
holding three or more positions increased across the 10-week
period.
of the distinct trends in trading behavior between Figures 2 and
3 demonstrates a
strong learning dynamic among the student population. Figure 3

shows the number
of positions (loss bands) held by market participants each week.
We can see quite
clearly that there is a strong trend among participants to
decrease exposure to a specific
loss band. This trend coincides with drop in the number of
trades undertaken in each
week, observed in Figure 2. This shows that market participants
are recognizing the
uncertainty of the environment, and although they may use
historical data as a guide,
they are managing their exposure by selecting a wider range of
loss bands. In this
context, the fall in the number of trades undertaken by
participants appears to be a
recognition that the difficulty in profiting by actively trading
insurance exposures based
on sparse information that is available to all participants.
FIGURE 4
Average Number of Positions Held per Week
Note: Participants are ranked and grouped by performance.
Given the sparse historical data available, the ILM environment
is one of Knightian
uncertainty and it forces participants to evaluate and manage
risk without recourse to
robust statistical measures. In the early weeks of ILM activity,
participants relied heavily
on the most recent weeks’ loss experience. Activity centered on
one or two loss bands

while those loss bands that appeared distant from recent
experience remained untraded.
Participants were undertaking highly risky behavior where a
minor weather event could
easily counter their market position. The increasing use of
diversification as a mechanism
for managing risk is one of the key outcomes from the market.
Furthermore, when
market participants are grouped according to performance, we
can see that those who
performed strongest over the 10-week period demonstrated the
greatest engagement in
overall diversification.
Weekly performance was based on the value of each
participant’s portfolio when the
markets were resolved at 17:00 GMT each Friday as
summarized in Equation (1). Figure 4
illustrates the trading behaviors of participants ranked by their
overall performance.
Those who performed strongest, the top 20th percentile,
engaged in a markedly higher
level of diversification. This provides robust evidence of the
validity of the ILM as a
teaching methodology in risk assessment and risk management.
CONCLUSIONS
This article describes the creation of a market in insurance
losses and its application in
risk management education. The unique application of real-time
insurance losses and
prediction market technology allowed students to explore the
practical considerations
in managing and trading insurance exposures. Incorporating this
teaching instrument
into university education has clearly had a positive impact in

engaging students in the
subject area and teaching them about the dynamics underlying
the insurance system.
More broadly, the use of prediction market technology in risk
management education
is shown here to improve critical thinking and provide an
important starting point for
introducing students to more sophisticated risk modeling and
risk management tech-
niques. The availability of historical insurance loss data through
commercial providers
such as Xactware as well as the wide number of prediction
market software means
that the project described here can be applied in other
universities. Furthermore, this
approach to augmenting the teaching of risk management can by
operated as a joint
venture among universities, thus allowing a larger number of
participants to forecast,
trade, and discuss insurance risks in an educational setting.
REFERENCES
Barth, M., J. Hatem, and B. Yang, 2004, A Pedagogical Note on
Risk Framing, Risk
Management and Insurance Review, 7(2): 151-165.
Born, P., and W. Martin, 2006. Catastrophe Modeling in the
Classroom, Risk Management
and Insurance Review, 9(2): 219-229.

Christiansen, J. D., 2007, Prediction Markets: Practical
Experiments in Small Markets
and Behaviours Observed, Journal of Prediction Markets, 1(1):
17-41.
Eckles, D., and M. Halek, 2007. The Problem of Asymmetric
Information: A Simulation
of How Insurance Markets Can Be Inefficient, Risk
Management and Insurance Review,
10(1): 93-105.
Hanson, R., 2007, Logarithmic Market Scoring Rules for
Modular Combinatorial Infor-
mation Aggregation, Journal of Prediction Markets, 1(1): 3-15.
Hoyt, R., L. Powell, and D. Sommer, 2007, Computing Value at
Risk: A Simulation
Assignment to Illustrate the Value of Enterprise Risk
Management, Risk Management
Joaquin, D., 2007, Loss Modeling Using Spreadsheet-Based
Simulation, Risk Management
Kinchin, I. M., D. Chadha, and P. Kokotailo, 2008, Using
PowerPoint as a Lens to Focus
on Linearity in Teaching, Journal of Further and Higher
Education, 32(4): 333-346.
Knight, F. H., 1921, Risk, Uncertainty, & Profit (New York:
Harper & Row).
Pennock, D. M., 2004, A Dynamic Pari-Mutuel Market for
Hedging, Wagering, and In-

formation Aggregation, in: Proceedings of the 5th ACM
Conference on Electronic Commerce
(New York: ACM), pp. 170-179. doi:10.1145/988772.988799.
Russell, D., 2000, Two Classroom Simulations in Financial Risk
Management and Insur-
ance, Risk Management and Insurance Review, 3(1): 115-124.
Copyright of Risk Management & Insurance Review is the
property of Wiley-Blackwell and its content may not
be copied or emailed to multiple sites or posted to a listserv
without the copyright holder's express written
permission. However, users may print, download, or email
articles for individual use.
Risk Management and Insurance Review
3, 413-433
DOI: 10.1111/rmir.12110
FEATURE ARTICLE
DRIVERLESS TECHNOLOGIES AND THEIR EFFECTS ON
INSURERS AND THE STATE: AN INITIAL ASSESSMENT
Martin F. Grace
Juliann Ping
ABSTRACT

This article explores the impacts of new auto technologies and
their financial
effects on insurance markets, a set of complementary services,
and state rev-
enues. We use data from the National Association of Insurance
Commissioners,
the National Highway Traffic Safety Administration’s Fatality
Analysis Report-
ing System, the Bureau of Justice Statistics, and the Census
Bureau to create
a data set that links industry and state finance variables to a set
of variables
related to driving. Our purpose in this initial assessment is to
estimate the sen-
sitivity of these financial variables to different indices of
driving including the
number of drivers, the number of cars licensed per year, and the
number of
vehicle miles driven. The resulting estimates are used to create
elasticities to
show how sensitive each is to changes brought about by the new
technologies.
INTRODUCTION
One of the most salient social risks, the risk of automobile
crashes, is predicted to
change with the introduction of new driverless or autonomous
technologies. Also, other
benefits associated with of driverless technologies may also
reduce other costs associated
with driving such as its associated pollution, the demand for oil,
and the widespread
productivity losses due to both traffic congestion and crashes.
This article attempts to
document the effect of driverless technologies on insurance

markets specifically as well
as state revenues and services related to automobile insurance.
As a first endeavor, we
try to analyze the macro effects of a reduction in driving
activity and its corresponding
impact on losses and other types of accident-related
expenditures.
The United States experiences a significant cost due to auto
crashes. A National High-
way Traffic Safety Administration (NHTSA) report (2015)
estimates the cost of driving
crashes to be about $836 billion in 2010 (in 2018 dollars, $960
billion), which—in addition
to the deaths, injuries, and property damages—also includes
costs due to pollution, con-
gestion, and reductions in quality of life. One of the reasons
autonomous vehicles are so
Martin F. Grace is the Harry Cochran Professor of Risk
Management at Fox School of Business,
Temple University, Philadelphia, Pennsylvania; e-mail:
[email protected] Juliann Ping
is a research assistant in the Department of Risk, Insurance and
Healthcare Management at Fox
School of Business, Temple University, Philadelphia,
Pennsylvania.
413
interesting is because of their potential for significantly
reducing these costs. Evidence

that even the lowest level of automation, so-called Level 1
automation, which implies
one automatic activity (like automatic braking systems [ABS],
blind spot monitoring,
lane departure warning, or forward collision warning) has
reduced crashes.1
Manufacturers claim that self-driving cars will be significantly
safer than human-driven
cars as driverless technology will allow for more precise driving
and quicker deci-
sion making. This increase in safety potential reduces the
propensity for auto crashes
(Litman, 2014). However, self-driving cars in combination with
human-driven cars on
today’s public roads may temporarily hinder the ideal prospects
of a driverless society.
Conjecture exists that most self-driving cars will produce lower
noxious emissions as
the cars will be designed as lightweight, two-passenger vehicles
(Burns, 2013). Further,
these cars could be 10 times more energy efficient than today’s
typical car (Burns, 2013).
Additionally, since one need not "drive" a self-driving car, the
opportunity cost of transit
will be diminished (Frisoni et al., 2016). Driverless technology
thus becomes an attractive
opportunity for automakers and consumers alike.
By utilizing the Society of Automotive Engineers (2016)
international levels and defi-
nitions of driving automation, we can approach the projections
of autonomous driving
with more uniformity and clarity. The levels are as follows:
1. Level 1: driver assistance,

2. Level 2: partial automation,
3. Level 3: conditional automation,
4. Level 4: high automation,
5. Level 5: full automation.
Different projections have been announced by various vehicle
and auto parts manufac-
turers on their products and plans. Table 1 illustrates the level
of automation that each
manufacturer expects to release in the form of a fleet of cars for
either taxis or commercial
sale.
As seen in Table 1, the majority of manufacturers estimate their
releases of Level 4 vehicle
technology to be by 2020. Waymo, the division of Alphabet, has
already released a fleet
of autonomous cars without safety drivers for testing in the
Phoenix, Arizona metro area
(Ohnsman, 2017). Levels 1 and 2 are being used in vehicles
today. These technologies
range from ABS to lane monitoring to unassisted parking. Level
3 represents a car that
the driver can shift certain functions to the vehicle to carry out
but is still able to take
over if needed.
Levels 4 and 5 have significant automation capabilities, and the
difference between
them lies in the fact that Level 5 automation requires self-
driving cars to be reliable in
all driving conditions (i.e., bad weather or a rural environment).

Before cars advance
1 ABS, for example, while not effective in reducing fatal
crashes, reduce nonfatal crashes by 6-8
percent (NHTSA, 2009). See also Harper et al. (2016) who
conclude that these Level 1 technologies
could reduce fatal crashes by over 10,000 per year.
DRIVERLESS TECHNOLOGIES AND THEIR EFFECTS ON
INSURERS AND THE STATE 415
TABLE 1
Automation Level Projections According to Manufacturers
Year 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026
2027 2028 2029 2030
Audi 2 3 3 3.5 3.5 3.5 3.5 3.5 3.5 3.5 4 4 4 4
Daimler/Uber 4 4 4 4 4 5 5 5 5 5 5
Delphi/MobilEye 4 4 4 4 4 4 4 4 4 4 4 4
Ford/Lyft 4 4 4 4 4 4 4 4 4 4 4
General Motors 4
Hondaa 2 2 2 3 3 3 3 3 3 3 3 3 3 3
Hyundai 2 4 4 4 4 4 4 4 4 4 4 4.5
Kia 2 2 2 3 3 3 3 3 3 3 3 3 3 4
Mercedes-Benz 3

Nissan 3 3 3 4 4 4 4 4 5 5 5 5 5 5
NuTonomy (Delphi) 4 4 4 4
Nvidia 5 5 5 5 5 5 5 5 5
Otto (Uber) 5 5 5 5
Tesla 3 4
Toyota 3 3 3 3 3 4 4 4 4 4 4
Volvo/Uber 4 4 4 4 4.5
Source: Jaynes (2016), Kessler (2017), Khalid (2017), Kubota
(2015), McFarland (2016), Payne (2017),
Ron (2017), Ross (2017), Valdes-Dapena (2017), Walker
(2017), Yu, Kim, and Ananthraraman (2017),
Ziegler (2016), and Zimmer (2016).
aHonda estimated that Honda vehicles would experience no
crashes by 2040.
to the Level 5 technology standard, we can at least expect that
Level 4 technology will
be increasingly utilized in densely populated cities and
preprogrammed routes through
large fleets and limited navigation routes.
Widespread implementation of self-driving vehicles into the
market will likely be limited
due to initial high costs, slow fleet turnover (cars currently on
the road), and design of
safety requirements (Litman, 2014) and the actual
implementation of these requirements
(NCOIL, 2017). Further, any fatal accidents caused by

experimentation like that of the
experimental Uber car in the spring of 2018 may cause
temporary halts to technological
experimentation until immediate safety concerns are met.
Together, this creates a poten-
tially significant cost increase and a steep learning curve to the
large-scale adoption of
autonomous vehicles by everyday consumers.
While the costs of implementation are significant, some markets
are directly connected
to the growth of the use of self-driving vehicles. Arguably, self-
driving cars will be safer
and less expensive to insure. Google claimed that its self-
driving Waymo will cut U.S.
auto crashes and deaths by 90 percent (Poczter and Jankovic,
2014).
Auto insurers will see a decrease in claim payouts, and there is
a suggestion that we
can expect premiums to drop significantly to as low as 90
percent of today’s typical
car insurance premium (Poczter and Jankovic, 2014). Because
insurance is typically a
“cost + markup” business, the reduction in costs will reduce the
total profitability of
auto-related insurance.
Other industries will likely be affected. The healthcare industry,
for example, could lose
patients and revenue because of the decrease in crashes

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