Value proposition of analytics in P&C insurance

A report on the value proposition of analytics in
P&C insurance
Gregg Barrett

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Executive summary
The Property and Casualty (P&C) insurance industry is facing numerous and significant challenges.
These challenges are imposing changes on the traditional way of doing business and are forcing
insurers to attain new capabilities in order to remain competitive. Analytics is one area that has, in a
short period of time, become a required competency across the insurance organization. Analytics is
now not only a necessity for all insurers but it is a source of competitive advantage for those that are
leading the field.
This report provides support to the analytics project covering claims management and fraud. The
report draws on the work of a number of industry sources and consists of six sections. Section I deals
with the challenges confronting the industry and the value of analytics. Section II covers the topic of
data and what is frequently referred to as “big data”. Section III looks at analytics as it relates to the
customer, underwriting and claims and section IV looks at spending on analytics and technology.
Section V examines the capabilities of leaders in the field of analytics and section VI concludes with a
look at some of the issues impacting successful implementation of analytics.

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Acknowledgment
This report draws extensively, and focuses on, the work and viewpoints from industry participants
including:
Accenture
American Institute for Chartered Property Casualty Underwriters
Charles Nyce
Chartered Insurance Institute
Coalition Against Insurance Fraud
Deepak Ramanathan
Deloitte
Economist Intelligence Unit
Ernst & Young
Gartner
IBM
Insurance Information Institute
John Standish Consulting
Massachusetts Institute of Technology
Michael A. Costonis
National Insurance Crime Bureau
Ordnance Survey
Roosevelt C. Mosley
SAS
Strategy Meets Action
References are included in-text as well as in the “References” section at the end of the report.

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Section I
Challenges facing the industry
Difficult and uncertain economic conditions, low interest rates, decreasing underwriting profitability,
higher combined ratios and low investment returns are placing insurers under stress. Insurers also
have to confront commoditisation of the business, more informed consumers, high customer churn
rates, new distribution channels and strong competition. If this was not enough natural perils,
increases in regulatory intervention and greater demands for transparency by regulators, together
with ever increasing compliance requirements are placing immense strain on the capabilities of
insurers.
According to IBM (2013) to thrive in this environment insurers must gain a specific set of capabilities
that will allow them to:
• Build a customer-centric business model
• Find profitable ways to sustain growth
• Develop new, competitively priced products
• Increase claims efficiency and effectiveness
• Improve capital management and investment decisions
• Improve risk management and regulatory reporting
(IBM, 2013, pg. 2)
Insurers are turning to analytics
The business of insurance is based on analyzing data to understand and evaluate risks. Two important
insurance professions, actuarial and underwriting, emerged at the beginning of the modern insurance
era in the 17th century. These both revolve around and are dependent upon the analysis of data.
(Strategy Meets Action, 2012, pg. 3)
While the insurance industry has long been recognized for analyzing data, the new news involves the
overwhelming amount of data that is now available for analysis and the sophistication of the
technology tools that can be used to perform the analysis. The opportunities for advanced analysis
are many and the potential business impact is enormous.
(Strategy Meets Action, 2013, pg. 3)
What is analytics?
Analytics: The use of data and related insights developed through applied analytics disciplines (for
example, statistical, contextual, quantitative, predictive, cognitive and other models) to drive fact-
based planning, decisions, execution, management, measurement and learning. Analytics may be
descriptive, predictive or prescriptive.
(IBM, 2011, pg. 2)

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Analytics domains in insurance
Strategy Meets Action (2012) classifies the analytical domains and the associated areas of analytical
application relevant to P&C insurance into; customer, risk and finance.
Figure 1: Analytics domains and opportunities in insurance
Figure 1. Analytics Domains and Opportunities in Insurance. Copyright 2012 by Strategy Meets Action. Reprinted with
permission.
The impact of analytics on the insurance life cycle
According to Accenture (2013) analytics has the potential to make a positive impact on virtually every
aspect of the insurance life cycle, including:
Product development
Analytics can help insurers tap into the wisdom of crowds to develop new products that speak to
genuine needs, and bring in new business.
Marketing and distribution
Real-time analytics and the use of sophisticated hypotheses bring one-to-one marketing at scale
within reach.
Pricing and underwriting
The combination of telematics and analytics enables the customization of mass-market products like
vehicle insurance and ancillary services.
Risk control
Analytics has an obvious role to play in identifying potential losses and, more important, putting
strategies in place to avoid them.

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Claims management
The general application of analytics, with particular focus on social networks and geospatial
information, can help insurers reduce claims fraud.
Performance management
Combining what-if analytics, visualization and unstructured data, insurance carriers can develop easy-
to-understand, actionable insights by role in order to make optimal use of scarce and expensive
human capital. In these and other areas, analytics confers on insurers the ability to improve
underwriting, claims and distribution outcomes.
(Accenture, 2013, pg. 5)
Organizations that adopt analytics achieve significant benefits
According to research from the Economist Intelligence Unit and IBM (2012), organizations that adopt
analytics achieve significant benefits compared those do not, including:
• 1.6 times greater revenue growth
• 2 times greater EBITDA growth
• 2.5 times stock price appreciation
This parallels with an earlier research project and survey of over 3,000 executives undertaken by MIT
Sloan Management Review and IBM (2011) which revealed the following:
• Organizations that substantially outperform industry peers are 2.2x as likely to agree that the
use of business information and analytics differentiate them in their sector.
• Analytically sophisticated organizations are 260 percent more likely to outperform their
competition than analytics beginners.
• Organizations that outperform their industry peers are 84 percent more likely to have business
analytics integrated into their core business strategy and day-to-day processes.
• Between 2010 and 2011, the number of executives who think analytics creates competitive
advantage rose 57 percent. (See figure 2 below.)
• Over half of executives polled said that “improvement of information and analytics was a top
priority in their organizations.”
(IBM, 2012, pg. 4)

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Figure 2: Creating a competitive advantage
Figure 2. The ability of organizations to create a competitive advantage with analytics has surged. Copyright 2011 by MIT.
Reprinted with permission.
Analytics and data mining the top technology priority
Not surprisingly then, according to Gartner's 2013 CIO survey of 86 respondents from life and P&C
insurers around the globe, analytics and data mining are considered the top technology priority for
the next three years.
(Gartner, 2014)

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Section II
Data is a key component
According to Strategy Meets Action (2012), the key to capitalizing on analytics is the data itself – the
ability to acquire, standardize, organize, and enhance both internal and external data.
IBM (2012) has an approach to analytics called “Smarter Analytics”. This smarter approach means the
ability to apply analytics to all of an organization’s data - not just the structured data from databases
or applications, but also the more elusive data that exists outside a business’s four walls: things like
weather patterns, economic trends, even tweets.
(IBM, 2012, pg. 2)
Figure 3: An IBM (2011) representation of an information supply chain for P&C insurance:
Figure 3. An information supply chain covers four segments of the information cycle: create, gather, package and provide
and consume. Copyright 2011 by IBM Corporation. Reprinted with permission.
Data, data, everywhere… If the use of analytics to manufacture business insights is the engine that will
drive profitable growth in a soft market, data is the fuel that makes the engine run. Unfortunately,
many in the insurance industry feel about data the same way that the Ancient Mariner felt about the
ocean: “…water, water, everywhere, nor any drop to drink.”
(IBM, 2011, pg. 4)
A data management strategy
Gartner (2014) has found that many insurance IT departments lack a consistent, enterprise-wide
business intelligence and data management strategy, because of siloed, line of-business-centric IT
systems.
(Gartner, 2014, pg. 6)
A potential solution to the issue of poor data management is to create what the Economist Intelligence
Unit (2013) calls a “data driven culture”.

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According to the report by the Economist Intelligence Unit (EIU), the strategies listed in figure 4 have
been ranked as successful in promoting a data driven culture.
Figure 4: Strategies to promote a data driven culture
Which strategies have proved successful in promoting a data-driven culture in your organization?
(% respondents)
Figure 4. Which strategies have proved successful in promoting a data-driven culture in your organization? Copyright 2012
by Economist Intelligence Unit. Reprinted with permission.
According to the EIU report the following should be considered as the crucial messages:
• Data-driven companies place a high value on sharing. Companies own data, not employees.
Data are a resource that can power growth, not something to be hoarded.
• Shared data should be utilised by as many employees as possible, which in practice means
rolling out training wherever it is needed.
• Data collection needs to be a primary activity across departments
• Perhaps most importantly, implementing a data driven culture requires buy-in from the top;
without that, little will change.
(Economist Intelligence Unit, 2013, pg. 11)
Challenges implementing a data driven culture
Implementing a data driven culture is not without its’ challenges though. Respondents to the EIU
survey ranked the following (see figure 5) as being sources of problems:

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Figure 5: If your organization faces problems realising a data-driven culture, which of the following
statements describe the origins of these problems?
Figure 5. If your organization faces problems realising a data-driven culture, which of the following statements describe the
origins of these problems? Copyright 2012 by Economist Intelligence Unit survey. Reprinted with permission.
The volume, variety and velocity of data – “big data”
The escalating volume, variety and velocity of information generated in today’s hyper-connected
world presents businesses with a major challenge. For those organizations wise enough to capitalize
on it, this abundance of information offers a major opportunity to gain competitive advantage. Big
data and the powerful analytics solutions now being used to analyze it are fundamentally changing
the way organizations manage their daily operations, where they direct new investments and even
how they are structured.
(IBM, 2013, pg. 2)
Why the era of big data is different
Two important trends make this era of big data quite different:
• The digitization of virtually “everything” now creates new types of large and real-time data
across a broad range of industries. Much of this is non-standard data: for example, streaming,
geospatial or sensor-generated data that does not fit neatly into traditional, structured,
relational warehouses.
• Today’s advanced analytics technologies and techniques enable organizations to extract
insights from data with previously unachievable levels of sophistication, speed and accuracy.
(IBM, 2012, pg. 3)

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Figure 6: Big data in dimensions
Figure 6. Four dimensions of big data. Copyright 2012 by IBM. Reprinted with permission.
Impact of data on insurance
A survey by Ordnance Survey and the Chartered Insurance Institute (2013) found:
• 68% said real-time location-based data could revolutionise understanding of cumulative risk
exposure in motor.
• 82% of respondents believe those insurers that do not capture the potential of big data will
become uncompetitive.
• 86% agree that the key to making best predictive use of big data is to be able to analyze data
from all sources together rather than separately.
• Motor (88%) and household (76%) are the insurance lines where pricing accuracy could be
transformed by big data pricing models.
• 88% agree that linking information by location is key to usefully combining disparate sources
of big data.
• Nine out of 10 respondents said access to real-time claims data would help price risk more
accurately.
• 96% of respondents say the digitally enabled world will see the emergence of new risk rating
factors.
Big data supplements traditional systems
Big data doesn't replace traditional data and analytics. As a relatively new set of technologies, one
might expect to see Hadoop, MapReduce and NoSQL databases dominate the landscape; however,
big data technologies are not really replacing incumbents such as business intelligence, relational

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database management systems and enterprise data warehouses. Instead, they supplement traditional
information management and analytics.
(Gartner, 2013, pg. 13)
Big data requires a different approach
Business analysts have typically worked to a requirements-based model, answering clearly-defined
business questions. Big data, however, demands a different approach, using opportunistic analytics
and exploring answers to ill-formed or non-existent questions.
Table 1: Characteristics of the Traditional versus the Big Data Approach
Note. Retrieved from Big data business benefits are hampered by 'culture clash'. Copyright 2013 by Gartner. Reprinted with
permission.

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Figure 7: Culture change - Discovery versus control
Figure 7. A better assessment of the data around and connected to a single piece of information enables a more complete,
in-context understanding. Copyright 2013 by IBM. Reprinted with permission.
Emerging techniques in big data and insurance
Some emerging techniques include:
• Predictive modelling: already well used by insurance companies, this works even better when
more data is fed into the model.
• Data-clustering: automated grouping of similar data points can provide new insights into
apparently familiar situations. Livehoods.org is an example of how social media and ‘machine
learning’ can reveal previously-unseen patterns.
• Sentiment analysis: textual keyword analysis can help analyze the mood of Twitter chatter on
a given topic or brand.
• Web crawling: sophisticated programmes that can identify an individual’s ‘web footprint’ as a
result of posting on social media websites, blogs and photo-sharing services. Using data-
matching, this can be linked to public records and data from other third parties to build a
multi-dimensional profile of an individual.
(Ordnance Survey, 2013, pg. 22)
Proceeding with care
Gartner (2013) provides the following recommendations for organizations moving forward with big
data initiatives:
• Information leaders should actively broker a balance between the marketing, risk and legal
departments to serve customers better and avoid the potential reputation risk in crossing the
"creepy line" and legal consequences.

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• Through ethical debate, information leaders should, together with marketing and legal,
develop and communicate a code of conduct for big data analytics, including principles and
guidelines.
• In their information governance practices and policies, information leaders need to set up a
sign-off function for results coming from automated analytical processes, to prevent illegal
and embarrassing results being shared.
Case study: Issues with Target data collection
Early 2012, the New York Times published a story highlighting an example of Target supermarkets that
is able to predict pregnancy from shopping behaviour. In Minneapolis, a father made a complaint
against a Target supermarket, upset that his 16-year-old daughter received discount coupons for
pregnancy items. It turned out the system was right, but is the correctness of an analysis the only
objective?
Table 2: Target data collection
Note. Retrieved from Privacy and ethical concerns can make big data analytics a big risk too.. Copyright 2013 by Gartner.
Case study: Issues with Google Street View
After several years of citizen complaints and government rulings in multiple countries about the
photographs of human faces appearing in Google's Street View, the service became increasingly
subject to government investigations globally. In 2010, it became known that Google was not only
collecting Wi-Fi router addresses and locations, an acceptable form of big data used to facilitate the
speed and accuracy of device location mechanisms, but was storing traffic data from those private
wireless networks. This huge store of unnecessary data led to investigations in at least 12 countries.
Although there were no findings of a direct impact on the individuals or institutions using these
wireless networks, at least nine countries determined that storage of this data was a regulatory
violation. Google did publicly announce it was a mistake and that the Wi-Fi traffic was collected in
error.

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Table 3: Google Street View
Note. Retrieved from Privacy and ethical concerns can make big data analytics a big risk too. Copyright 2013 by Gartner.
Case study: Issues with TomTom data collection
Navigation system manufacturer TomTom collects real-time data from its’ devices to show traffic jams
based on slowing driving speeds. Its’ terms and conditions state the data can be aggregated,
anonymized and sold. There were no issues with the Department of Traffic using the data to analyze
issues around roadwork. However, when the police acquired the data to measure how fast people
were driving, and newspapers reported negatively on it, TomTom was forced to change its’ terms and
conditions.
Table 4: TomTom data collection
Note. Retrieved from Privacy and ethical concerns can make big data analytics a big risk too.. Copyright 2013 by Gartner.
Big data to fight fraud
According to John Standish Consulting (2013), mobilizing big data is gaining wider attention in anti-
fraud circles. Insurers are sitting on troves of data, hard and soft. Much is never accessed for fraud-

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fighting. Insurers can dramatically increase their anti-fraud assertiveness by insightfully accessing,
analyzing and mobilizing their large volumes of untapped data.
Marshaling analytics and big data with current rules and indicators into a seamless and unified anti-
fraud effort creates an expansive world of possibilities.
• Imagine the ability to search a billion rows of data and derive incisive answers to complex
questions in seconds.
• Imagine being able to comb through huge numbers of claim files quickly.
• Imagine more-quickly linking numerous ring members and entities acting in well-disguised
concert. These suspects likely could not be detected with sole or even primary reliance on
basic methods such as fraud indicators.
• Ultimately, imagine analyzing entire caseloads faster and more completely, thus addressing
the largest fraud problems and cost drivers in any of an insurer’s coverage territories.
(Standish, 2013)
According to Gartner (2013) big data and the associated technology has been shown to provide the
following benefits:
• Detection and prevention of fraud or other security violations
• High ROI
• Little operational disruption
Case study: Fraud at IBC
The Insurance Bureau of Canada (IBC) is the national insurance industry association representing
Canada’s home, car and business insurers. Because investigation of cases of suspected automobile
insurance fraud often took several years, the company’s investigative services division wanted to
accelerate its’ process. The IBC worked with IBM to conduct a proof of concept (POC) in Ontario,
Canada that explored new ways to increase the efficiency of fraud identification. The POC showed
how IBM solutions for big data can help identify suspect individuals and flag suspicious claims. IBM
solutions also help users visualize relationships and linkages to increase the accuracy and speed of
discovering potential fraud. In the POC, more than 233,000 claims from six years were analyzed. The
IBM solutions identified more than 2,000 suspected fraudulent claims with a value of CAD41 million.
IBM and the IBC estimate that these solutions could save the Ontario automobile insurance industry
approximately CAD200 million per year.
(IBM, 2012)

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Section III
Looking at the customer
It is not just about pricing
The reality of today's insurance marketplace resembles a Hobbesian, dog-eat-dog world where
companies are competing for a finite pool of possible customers. It's a zero-sum game where growth
strategies are largely built upon a company's ability to win business from competitors and defend
against similar incursions into its’ own market share. The main weapon in this battle has traditionally
been pricing - and this is precisely where those insurance companies that leverage big data and
analytics focus their efforts, often to the exclusion of claims, products, agents and sales operations,
among other areas.
The message for the insurance industry is clear: pricing is not the silver bullet. Big data and its’ benefits
can help insurers become more informed, more nimble and better able to interact meaningfully with
consumers.
(Ramanathan , 2013)
Improving customer retention
Common sense suggests that the first step in growing the portfolio of quality customers is to first
prevent attrition of current quality customers. Insurance companies can more successfully grow their
books of business by doing a better job of retaining profitable customers and developing programs to
retain those customers while working to increase revenue through more effective customer up-sell
and cross-sell strategies.
By better understanding and predicting the behaviours and needs of key, at-risk customers, insurance
companies can prevent churn and develop outbound marketing campaigns to increase revenues.
But there are other related benefits of improving customer retention and satisfaction:
• Customer retention is far cheaper than customer acquisition
• A loyal customer is a strong competitive advantage because of the recurring revenue potential
• A satisfied customer can become “part of the team,” helping to sell your value proposition by
word-of-mouth referrals
• Customers are a great source of feedback so you can continuously improve your service
standards
• Customers also provide market intelligence feedback on competitive offerings so you can keep
abreast of market dynamics
According to IBM (2010) organizations are using predictive analytics to answer key customer questions
about each individual customer, such as:
• Is this customer likely to leave? If so, why?
• And what offer is most likely to persuade him-or-her to stay?
• And given the customer’s predicted profitability and risk profile, what retention effort is
justified?
(IBM, 2010, pg. 2, 4, 7)

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Case study: Improving retention
A large US insurer conducted extensive analysis on customer information files, transaction data and
call-center interactions to identify customers who would respond positively to contact with an agent.
Based on the analysis, the company then developed new product offers. The result was a significant
increase in offer response rates and up to a 40 percent retention rate improvement.
(IBM, 2013, pg. 3)
Customer data
According to Gartner (2013) customer data comes from areas such as:
• Transaction data
• Facebook "likes," Pinterest, LinkedIn, Twitter feeds and posts
• YouTube creations and views — in fact, all video streams, many viewed on mobile
• Psychographic analyses
• GPS coordinates
• Embedded data streams from sensors embedded in clothing and devices — for example,
home electronics and vehicles
• Forum posts
• Social network analyses
Gathering the data is one thing, in order to make sense of it, it is critical to contextualise the data in
order to obtain a consistent view of the customer.
Figure 8: A consistent view of the customer
Figure 8. Consistent view of the customer for big data. Copyright 2013 by Gartner. Reprinted with permission.

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Using social media analytics for customer service
One example of the use of social media analytics is the customer reaction to company advertising
campaigns. As customers provide feedback on advertising and commercials, for example, companies
can use this raw, unsolicited feedback to make their marketing programs more effective. Also, if
customers are particularly happy or unhappy with a company about a particular issue, it provides the
company with an opportunity to attempt to proactively address this issue before it takes on a life of
its’ own.
From a broader perspective, the use of social media can be used to identify broader trends in the
market that the company may be able to take advantage of. An example of this might be an influx of
social media data that suggests more people are looking for quotes or shopping for insurance in a
particular area, or identifying concerns with finding affordable insurance in a particular area. Again,
this could be brought forward to the right area within a company and proper steps taken to respond
to these market trends
(Mosley, 2012, pg. 26, 27)
Case study: Customer segmentation at Progressive
In July 2012, Progressive Insurance released new findings from an analysis of five billion real-time
driving miles, confirming that driving behaviour has more than twice the predictive power of any other
insurance rating factor. Loss costs for drivers with the highest-risk driving behaviour are approximately
two-and-a-half times the costs for drivers with the lowest-risk behaviour. These results suggest that
car insurance rates could be far more personalized than they are today.
Progressive has also found that 70% of drivers who have signed up for its’ Snapshot UBI program pay
less for their insurance. The program involves installing a small monitoring device in the car (900,000
drivers have already done this) and driving normally. After the device has collected enough data,
customers receive a personalized rate for their insurance. Progressive is currently expanding access to
Snapshot to all of its’ drivers - not just Progressive customers - who can take a free test drive of the
technology and after 30 days find out whether their own driving behaviour can lower the price they
pay for insurance.
The problem with today's less granular systems of customer classification in the property and casualty
insurance market is that the majority of drivers who present a lower risk subsidize the minority of
higher-risk drivers.
Analytics and adverse selection
As more insurers use predictive analytics, those not doing so will be increasingly exposed to adverse
selection because their market will be limited to a subsection for the general population that has
worse-than-average loss ratios.
(Nyce, 2007, pg. 3)

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Looking at underwriting
The focus on analytics has been on underwriting
As figure 9 from Accenture (2012) shows, insurers spend more than one half of their risk analytics
investments on underwriting.
Figure 9: Within the insurance functional area, where does your organization currently spend the
majority of its’ risk analytics investments?
Figure 9. Within the insurance functional area, where does your organization currently spend the majority of its’ risk analytics
investments?. Copyright 2012 by Accenture. Reprinted with permission.
Going forward insurer’s plans to embed analytics in actuarial and underwriting systems continue with
underwriting still being a major area of focus, according to Strategy Meets Action (2013).
Figure 10: Insurer Plans to Embed Analytics in Actuarial and Underwriting Systems
(Percent of Insurers Planning Projects, Next 24 Months)
Figure 10. Insurer plans to embed analytics in actuarial and underwriting systems. Copyright 2013 by Strategy Meets Action.

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Using analytics to prevent underwriting fraud
Insurance fraud continues to be a major issue for all insurance companies. Statistical data published
by the FBI estimates that insurance fraud (in the US) costs an estimated $40 billion per year, which
increases the average annual premium for consumers by an estimated $400 to $700. Similar figures
are available across the globe. To combat this issue, the current focus of most insurance companies
has been on detecting and preventing claims fraud. However, a significant amount of insurance fraud
is associated with underwriting fraud.
Recent studies have shown that insurers lose approximately 10 percent of their total revenue to
premium leakage and underwriting fraud. In 2010, rating errors reduced private passenger auto
insurance premium revenue in the US by $15.4 billion. But underwriting fraud is not exclusive to
personal auto insurance. It affects most lines of business, especially commercial auto insurance,
workers’ compensation and property insurance. According to Dennis Jay, Executive Director of the
Coalition Against Insurance Fraud, “Application fraud traditionally has been the poor cousin of claims
fraud, receiving little attention and not being fully understood. Forward-thinking insurers are
developing new strategies and employing new tools to not only detect underwriting fraud, but to
prevent it as well.”
(SAS, 2013, pg. 1)
According to the Insurance Information Institute (2013) uses of data analytics have been reported to
have produced reductions in fraud loss of 20 to 50 percent for some carriers.
Case study: Improving underwriting decisions
A large global property casualty insurance company wanted to accelerate catastrophe risk modelling
in order to improve underwriting decisions and determine when to cap exposures in its’ portfolio. The
current modelling environment was too slow and unable to handle the large-scale data volumes that
the company wanted to analyze. The goal was to run multiple scenarios and model losses in hours,
but the current environment required up to 16 weeks. As a result, the company conducted analysis
only three or four times per year. A proof of concept demonstrated that the company could improve
performance by 100 times, accelerating query execution from three minutes to less than three
seconds.
The company decided to implement IBM solutions for big data, and can now run multiple catastrophe
risk models every month instead of only three or four times per year. Once data is refreshed, the
company can create “what-if” scenarios in hours rather than weeks. With a better and faster
understanding of exposures and probable maximum losses, the company can take action sooner to
change loss reserves and optimize its’ portfolio.
(IBM, 2013, pg. 7)

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Looking at claims
Analytics can provide answers for questions at every stage of the claims lifecycle including:
Q: What is the right amount to reserve for an individual claim?
A: Suggests the reserve adequacy amount most appropriate given the full set of claim
characteristics to help control reserve creep with increasing risk exposure.
Q: Which resource is best suited to deal with an individual claim?
A: Identifies the right resources at the right time to reduce handling expenses.
Q: Should I assign this claim to an outside party?
A: Identifies which claims are likely to require external resources later on in the handling process.
Q: Should I investigate this claim for fraud?
A: Identifies claims with the highest likelihood of avoiding (full or partial) payout to reduce
unnecessary payouts.
Q: Which claims should I investigate first?
A: Identifies referrals with the highest likelihood of a successful investigation outcome.
Q: Should I subrogate this claim? Which ones should chase first?
A: Identifies the claims with the highest likelihood of successful recovery to help maximize the
amount recovered from third parties.
(IBM, 2011, pg. 8)
Claims fraud on the rise
According to the National Insurance Crime Bureau (NICB) in an analysis of its’ database of claims
released in May, 2013, questionable property-casualty claims in the U.S. have increased 27 percent in
2012 over 2010. That reflects 91,652 questionable claims in 2010 compared to 116,171 claims in 2012.
(NICB, 2013)
Consumer tolerance of claims fraud rises
According to Deloitte (2012) consumers are increasingly tolerant of claims fraud, whether it is “hard
fraud” such as claiming fictitious losses or “soft fraud” such as inflating the cost of genuine losses.
A survey by the Coalition Against Insurance Fraud (2008) found that roughly one in five U.S. adults felt
it was acceptable to defraud insurance companies under certain circumstances.
The survey also found that the number of consumers who felt various types of insurance fraud (e.g.,
misrepresenting facts on an insurance application, inflating a claim, or misrepresenting an incident to
be paid for an uncovered loss) were unethical had declined over the previous decade.

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Figure 11. Claims fraud may account for as much as 10% of a carriers Incurred loss expense…which is especially challenging
in today’s market with combined ratios rising well above 100. Copyright 2013 by IBM. Reprinted with permission.
Controlling claims fraud by implementing a four pillar framework
Deloitte (2012) suggest implementing an integrated framework with four pillars to control claims
fraud:
• A focused claims fraud management strategy with clear goals and priorities
• An operating model that integrates activities to mitigate claims fraud
• Enhancing information quality, access and consistency to support data analysis
• Leveraging data analytics to focus human attention on high risk customers and claims
(Deloitte, 2012, pg. 1)
Social network analysis in fighting claims fraud
Social network/ link analysis can be used in the fight against claims fraud. According to IBM (2013)
social network/link analysis can assist insurers by providing the following:
• Visual and mathematical analyses of complex human and computer networks
• Discover sources and distribution of power: Who knows whom; who does business with
whom; and who wields greatest power
• See how communications flow in networks and sub-networks
• Monitor patterns between communications nodes, performance and key goals
• Combines data extraction, manipulation, analysis and visualization
(IBM, 2013, pg. 14)

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One notable case was a bus claim where the driver turned out to be Facebook friends with 28 of the
30 passengers. We discovered he had sold seats on the bus to his friends for £500 a time in the hope
they would each win back £2 500 in injury claims!
(Ordnance Survey, 2012, pg. 15)
Figure 12: Social network/link analysis
Figure 12. Social network/link analysis. Copyright 2013 by IBM. Reprinted with permission.
Case study: Claims processing at Santam
Founded in 1918, Santam has grown to become South Africa’s largest short-term insurance company.
With more than 650,000 policy holders and assets under management of 17 billion South African
Rand, the company enjoys a market share of more than 22 percent. It offers customers a wide range
of services in personal, commercial, agricultural and specialist insurance and risk management.
Santam wanted to find a way to improve its’ service to customers by settling claims faster and keeping
premiums low. To achieve this, the company needed to maximize operational efficiency and find
smarter ways to combat fraud. Using IBM Business Analytics, the company implemented a claims
solution that captures data from incoming claims, assesses each claim against identified risk factors
and segments claims to five risk categories - separating likely fraudulent claims and higher-risk cases
from low-risk claims.
With the new system, the company not only saves millions previously lost to insurance fraud, but also
drastically reduces processing time for low-risk claims, leading to resolution in less than an hour for
some customers.
“In the first month of using the IBM Business Analytics solution, we were able to identify patterns that
enabled us to foil a major motor insurance fraud syndicate. Within the first four months, we had saved
R17 million on fraudulent claims, and R32 million in total repudiations - so the solution delivered a full
return on investment almost instantly!”
– Anesh Govender, Head of Finance, Reporting and Salvage, Santam Insurance
(IBM, 2013, pg. 7)

24
Case study: Claims processing at Infinity
Infinity Property & Casualty Corporation used business analytics to reduce payments on fraudulent
claims, reduce loss adjustment expenses and improve its’ ability to collect payments from other
insurance companies. Infinity achieved a payback on its’ predictive analytics investment in only three
months, and gained an annual ROI of 400 percent for direct and indirect benefits in the following
areas:
• Increase of $12 million in subrogation recoveries
• As much as 95 percent reduction in time required to refer questionable claims for
investigation
• Increase in success rate in pursuing fraudulent claims from 50 percent to 88 percent
• Ability to keep 25 percent of claims within the company’s first notice of loss area (up from 4
percent), enabling Infinity to sharply improve its’ Loss Adjustment Expenses (LAE) ratio
“Whether it’s fraud reduction, customer convenience or cost control, leveraging intelligence will be
increasingly important to the way we differentiate ourselves in the future. We’ve shown our
willingness to take some chances to make this happen and we have the results to show for it,” said
Bill Dibble, SVP of Claims Operations, Infinity Insurance.
(IBM, 2011, pg. 3)

25
Section IV
Spending trends on data and analytics
According to Strategy Meets Action (2013) there are two major trends: increased spending on data
and analytics, and broader usage of analytics beyond the traditional risk-centric applications. Most
P&C insurers plan to increase spending from 2013 through 2015, with 83% of P&C planning annual
increases (up from 2012). 16% of P&C insurers plan annual increases of more than 10% of their
budgets for data and analytics. Underwriting is still the number one business area for analytics
investment, but spending will increase in product development, marketing, and other areas.
There is no question that analytics projects are viewed as game-changers, but insurers also see the
need to focus on improving and enhancing their data to fully capitalize on the power of analytics.
(Strategy Meets Action, 2013)
Figure 13: The percent of insurers planning IT budget increases for data and analytics for 2013-2015.
Figure 13: IT spending plans for data and analytics (2013 through 2015). Copyright 2013 by Strategy Meets Action. Reprinted
with permission.

26
Section V
Capabilities of leaders in analytics
IBM (2013) identified nine levers that represent the sets of capabilities that most differentiated
leaders in analytics from other respondents:
• Culture: Availability and use of data and analytics within an organization
• Data: Structure and formality of the organization’s data governance process and the security
of its’ data
• Expertise: Development of and access to data management and analytic skills and capabilities
• Funding: Financial rigor in the analytics funding process
• Measurement: Evaluating the impact on business outcomes
• Platform: Integrated capabilities delivered by hardware and software
• Source of value: Actions and decisions that generate results
• Sponsorship: Executive support and involvement
• Trust: Organizational confidence
Further, IBM identified three levels of value impact among the nine levers:
• Enable levers form the basis for big data and analytics
• Drive levers are needed to realize value
• Amplify levers boost value creation
Figure 14: The nine levers are capabilities that enable and enhance analytics development, delivery
and value creation.
Figure 14: The nine levers are capabilities that enable and enhance analytics development, delivery and value creation.
Copyright 2013 by IBM. Reprinted with permission.
According to IBM, while it’s helpful to know how each lever influences value creation, it’s equally
important to consider where each lever fits in terms of the day-to-day aspects of running a business.
Most executives need to approach analytics with a business-driven blueprint, an approach that defines
how and why the organizations will use technology through three lenses: strategy, technology and
organization.
• Strategy: The deliberateness with which an organization approaches analytics

27
• Technology: The enabling capabilities and resources an organization has available to manage,
process, analyze, interpret and store data
• Organization: The actions taken to use data and analytics to create value
This construct, popularized as organizations built business intelligence foundations and other
enterprise applications, creates a blueprint that guides executives to consider both the strategic and
tactical actions needed to act on data, as well as define the business and technical requirements for
the use of analytics.
Figure 15: While it is important to understand how each lever impacts value, organizations implement change using a
different construct. Copyright 2013 by IBM. Reprinted with permission.
Strategy
The levers of Sponsorship, Source of value and Funding represent those capabilities needed to define
and enable a strategic approach to data and analytics. By emulating the behaviours of leaders within
these levers, executives can instill a sense of purpose to analytics that connects the strategic vision of
the executive suite to the day-to-day actions needed to act on analytics.
Technology
The levers of Expertise, Data and Platform combine to create the technical capabilities and resources
an organization has available to manage, process, analyze, interpret and store data. By identifying the
capabilities most needed to solve the organization’s unique requirements, executives can create a
foundation for analytic discovery to solve today’s challenges, while also architecting it for the future.
Organization
The levers of Culture, Measurement and Trust coalesce to form an organization’s ability to act on data
and analytics, which is the only way to realize a return on investment. Executives need to consider the
cultural impact and changes required to operate as a fact-driven organization and be able to measure
success when it occurs. It takes more than memos and measurement to transform an organization: it
takes trust. It takes trust in the data, but also trust in one another - trust that everyone is working
toward the same goal and similar outcomes.
By examining their own activities through the lens of the levers, organizations still struggling to
harness the insights buried in their data can begin building a value-based analytics strategy.
(IBM, 2013, pg. 3, 18, 19)

28
Centralized enterprise unit for analytics
An IBM and MIT Sloan study (2010) found that 63 percent more “Transformed” organizations than
“Aspirational” organizations use a centralized enterprise unit as the primary source of analytics and
that this can provide a home for more advanced skills to come together within the organization.*
The centralised enterprise unit provides both advanced models and enterprise governance by
establishing priorities and standards in the following ways:
• Advancing standard methods for identifying business problems to be solved with analytics
• Facilitating identification of analytic business needs while driving rigor into methods for
embedding insights into end-to-end processes
• Promoting enterprise-level governance on prioritization, master data sources and re-use to
capture enterprise efficiencies
• Standardizing tools and analytic platforms to enable resource sharing, streamline
maintenance and reduce licensing expenses.
(IBM, 2010, pg. 16)
* (Please see table 1 in the appendix for a definition of “transformed” and “aspirational” organizations.)

29
Section VI
Management and culture are the most challenging
According to IBM (2010) the adoption barriers organizations face most are related to management
and culture rather than data and technology. The leading obstacle to widespread analytics adoption
(see figure 16) is lack of understanding of how to use analytics to improve the business, according to
almost four of ten respondents. More than one in three cite lack of management bandwidth due to
competing priorities. Organizations that use analytics to tackle their biggest challenges are able to
overcome seemingly intractable cultural challenges and, at the same time, refine their data and
governance approaches.
(IBM, 2010, pg. 5)
Figure 16: The adoption barriers organizations face most are related to management and culture
rather than data and technology.
Figure 16: Respondents were asked “What are the primary obstacles to widespread adoption and use of information and
analytics in your organization? Please select up to three.” Copyright 2010 by MIT. Reprinted with permission.
According to an MIT and IBM research report (2011) organizational challenges like that of
management and culture are also the most difficult to resolve (see figure 17).

30
Figure 17: Changing the way people behave and interact with one another within an organization
poses a more difficult challenge than changing their tools or technologies.
Figure 17: Changing the way people behave and interact with one another within an organization poses a more difficult
challenge than changing their tools or technologies. Copyright 2011 by MIT. Reprinted with permission.
Talent mismatch
In general it seems clear that there is a critical mismatch between the supply of and demand for
specialized analytics talent (see figure 18).
Figure 18: Total shortages and surpluses of analytics talent by country, 2010 to 2015
The shortfall of analysts in the US will exceed the surpluses expected in India and China combined.
Data is for all types of analytics talent across all industries within each country
Figure 18: Total shortages and surpluses of analytics talent by country, 2010 to 2015. Copyright 2013 by Accenture. Reprinted
with permission.

31
Four steps insurers must take to recruit talent
Costonis (2013) suggests that insurers take four steps in order to address the talent shortage issue:
1. Influence universities to add business analytics and insurance coursework to a wide variety
of programs.
2. Get the most out of existing talent.
3. Raise insurance awareness among students.
4. Make sure insurance jobs are appealing to prospective students.
Insurers will need to fill as many as 400,000 positions by 2020, all while more “glamorous” industries
compete for the same talent pool.
(Costonis, 2013, pg. 33)
Analytics focus remains tactical rather than strategic
Insurance is trailing the average when it comes to successful use of analytics as part of an integrated,
enterprise-wide approach that is ingrained into the fabric of the company (see figure 19).
Q: Which of the following statements best describes what you are currently doing with analytics in
your own area of the business?
Figure 19: Insurance tends to trail other industries in the integrated, enterprise-wide use of analytics. Copyright 2013 by
Accenture. Reprinted with permission.

32
Insurers using analytics as a retrospective rather than a predictive tool
Insurance is lagging other industries when it comes to the most valuable use of analytics, namely the
ability to react to customer behaviour in real time - a capability that is becoming critical as
customers increasingly use mobile devices to transact and interact at times that suit them - and to
make better decisions. This is because insurers are still using analytics as a retrospective tool rather
than a predictive tool.
Q: In general, and looking at the use of analytics across the entire organization, would you say it is
primarily used as a predictive or a retrospective tool?
Figure 20: Most insurers are using analytics as a retrospective rather than a predictive tool. Copyright 2013 by Accenture.

33
Conclusion
Analytics is generating substantial value for the P&C insurance industry. Insurers are using analytics to
overcome many of the challenges confronting the industry, and to support new capabilities. Although
implementing analytics is not without its’ challenges, through careful consideration of the findings
and recommendations of those mentioned in this report, the organisation should be able to generate
an appreciable return on its’ analytics initiative. As shown with case studies examples of other carriers,
the data and technology toolsets are available, tried and tested.

34
Appendix
Table 1: Three capability levels, Aspirational, Experienced and Transformed, were based on how
respondents rated their organization’s analytics prowess
Note. Retrieved from Analytics: the new path to value. Copyright 2010 by MIT. Reprinted with permission.

35
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