This presentation provides a brief insight into the need to undertake an analytics project, particularly as it pertains to claims management and fraud. To this end the presentation will touch on the general challenges confronting the property and casualty insurance industry, as well as the challenges and lessons learnt from early adopters of business intelligence. In the face of these challenges analytics holds the potential to generate substantial value as evidenced by several short case study examples. The presentation concludes with a look at the issue of fraud as it pertains to the industry and some of the metrics that are influenced by it. The presentation draws extensively, and focuses on, the work and viewpoints from industry participants including; Accenture, IBM, Ernst & Young, Strategy Meets Action, Ordnance Survey, Gartner, Insurance Institute of America, American Institute for Chartered Property Casualty Underwriters, International Risk Management Institute and John Standish Consulting. References are included on each slide as well as on the “References” slides at the end of the presentation.

1. 1
Analytics
Potential value generation
P&C insurance
Gregg Barrett

2. Executive Summary
This presentation provides a brief insight into the need to undertake an analytics project at
Solace P&C, particularly as it pertains to claims management and fraud. To this end the
presentation will touch on the general challenges confronting the property and casualty
insurance industry, as well as the challenges and lessons learnt from early adopters of
business intelligence. In the face of these challenges analytics holds the potential to
generate substantial value as evidenced by several short case study examples. The
presentation concludes with a look at the issue of fraud as it pertains to the industry and
some of the metrics that are influenced by it.
The presentation draws extensively, and focuses on, the work and viewpoints from industry
participants including; Accenture, IBM, Ernst & Young, Strategy Meets Action, Ordnance
Survey, Gartner, Insurance Institute of America, American Institute for Chartered Property
Casualty Underwriters, International Risk Management Institute and John Standish Consulting.
References are included on each slide as well as on the “References” slides at the end of the
presentation.
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3. Challenges facing the industry
• The insurance value chain is under pressure.
• Carriers do not fully understand the impact of their marketing
investments.
• Carriers are slow to introduce new products and pricing models.
• Carriers are experiencing material losses due to fraud.
(Accenture, 2013, pg. 1)
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4. Industry technology challenges
Despite their hefty and increasing investments in data warehouses, architectures, analytics, and business intelligence (BI) platforms,
many insurance companies still are not getting the value they want, and need, from their BI initiatives.
In essence, past business intelligence initiatives in insurance basically amounted to the status quo: simple spreadsheets.
The promise of what business intelligence would bring to insurance is starkly different from today’s reality. Carriers were supposed to
have accurate data that would be:
• Easily accessible and shareable to all.
• Very specific, drilling down from summary to individual transactions.
• Actionable information, providing insights on where and how to improve business results.
• The foundation for data-rich solutions across the enterprise, helping to manage brokers, customers, and operations.
Lessons:
First, the emphasis of BI initiatives was on the technology rather than the real business asset: information.
Second, design of the new BI systems replicated the same segmented, isolated reports already being used by
department specific users instead of emphasizing enterprise-wide insight.
Third, BI was viewed as an IT project, guided and controlled by the IT organization rather than the enterprise.
(Accenture, 2012, pg. 2 – 3)
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5. Definition: 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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6. Analytics holds promise
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.
(American Institute for Chartered Property Casualty Underwriters/Insurance Institute of America, 2007, pg. 3)
Natural perils, globalisation, and disruption in distribution combined with regulatory intervention and
increased competition has put immense pressure on insurers. Rapid integration of technology and life has
created a proliferation of data, presenting unprecedented opportunities to use advanced analytics to
leverage new information – about potential markets, risks, customers, competitors and natural disasters.
(Ernst and Young, 2013, pg. 1)
The use of these advanced, high performance analytics capabilities and the potential they have to
augment and enrich customer insights, financial management, risk assessment, and day-to-day operations
mean that analytics is fast becoming THE competitive battleground for insurers.
(Strategy Meets Action, 2012, pg. 3)
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7. Analytics: competitive advantage
Figure 1. Respondents. Copyright 2013 by Ordnance Survey. Reprinted with permission.
Those insurers that do not take significant steps to improve access to new data sources and
sophistication in predictive analytics will become uncompetitive:
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8. Analytics: the enterprise view for insurance
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Figure 2. 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.

9. Analytics domains in insurance
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Figure 3. Analytics Domains and Opportunities in Insurance . Copyright 2012 by Strategy Meets Action. Reprinted
with permission.

10. The upside of analytics in insurance
Analytics has the potential to make a positive impact on virtually every aspect of the insurance life cycle.
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.
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)
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11. Case study: segmentation
Granular Segmentation at Progressive Insurance
In July 2012, Progressive Insurance released new findings from an analysis of five billion real-time driving miles,
confirming that driving behavior has more than twice the predictive power of any other insurance rating
factor. Loss costs for drivers with the highest-risk driving behavior are approximately two-and-a-half times the
costs for drivers with the lowest-risk behavior. These results suggest that car insurance rates could be far more
personalized than they are today.
(Gartner, 2013, pg. 5)
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12. Case study: improving retention
Improving retention by identifying the right customers
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)
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13. Case study: claims management and fraud
Auto insurer Infinity Property and Casualty sought a way to analyze and score insurance
claims faster in order to zero in quickly on suspected fraud and speed up the settlement of
valid claims.
With IBM predictive analytics, Infinity was able to:
• Double the accuracy of fraudulent claim identification and accelerate the referral of
suspicious claims to company investigators.
• Improve customer satisfaction and retention by paying legitimate claims faster, contributing
to above-average company growth.
• Generate a 403 percent ROI from reduction in claims payments and enhanced
subrogation results.
(IBM, 2013, pg. 3)
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14. Case study: fraud
“………….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)
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15. Fraud is a top concern
‘Insurers invest £200 million plus per year in their anti-fraud staff and systems… Those investments saved over £900 million in claims
payments in 2011.’
Phil Bird, Director, Insurance Fraud Bureau
The companies we surveyed place fraud high on the corporate agenda.
• Seven out of ten report that fraudulent activity has moved up their organisation’s agenda in the last 12 months and
74.5% report increased investment in fraud detection.
• 69% saw increased investment targeted at staff, 64% in fraud detection systems and 45% in front-end procedures.
• Location intelligence plays a key role in the fight against fraud, with 83% of respondents using geography.
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. 11, 15)
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Note. Retrieved from Insurance fraud 2012: On the rise opportunistic and online. Copyright 2012 by
Ordnance Survey. Reprinted with permission.
Table 1
The top-three concerns: recession, resources and policy inception

16. Fraud: the low hanging fruit
Benefits of this technology:
• Detection and prevention of fraud or other security violations
• High ROI
• Little operational disruption
(Gartner, 2013, pg. 5)
“When you leverage best practices and analytics together in insurance fraud investigations,
however, a powerful tool and business model is created that will create significant results to
reduce fraud and provide a great return on investment (ROI) in anti-fraud programs.”
(Standish, J, 2012)
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17. Claims management and fraud still to be fully exploited
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Figure 4. North American organisations spend more than one-half of their risk analytics investments on underwriting,
while distribution sees the least capital. Copyright 2012 by Accenture. Reprinted with permission.

18. Fraud metrics
Fraud impacts
o Loss ratio
Calculated as “incurred losses to earned premiums expressed as a percentage” (International
Risk Management Institute, 2014)
o Expense ratio
Calculated as the “percentage of premium used to pay all the costs of acquiring, writing, and
servicing insurance and reinsurance” (International Risk Management Institute, 2014)
o Combined ratio
Calculated as the “sum of two ratios, one calculated by dividing incurred losses plus loss
adjustment expense (LAE) by earned premiums (the calendar year loss ratio), and the other
calculated by dividing all other expenses by either written or earned premiums” (International
Risk Management Institute, 2014)
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19. Conclusion
It is my recommendation that an analytics project at Solace P&C covering claims
management and fraud be a priority. As shown with case studies examples of
other carriers, the data and technology toolsets are available, tried and tested,
and the returns are asymmetrical - substantial rewards with little risk. Successfully
applying analytics to these areas will result in favourable improvements in the loss
ratio, expense ratio and combined ratio.
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20. References
Accenture. (2012). Reaping the benefits of analytics: six ways to make your business intelligence smarter. [pdf]. Retrieved from
http://www.accenture.com/us-en/Pages/insight-reaping-benefits-analytics-six-ways-make-bi-smarter-summary.aspx
Accenture. (2012). North american organisations spend more than one-half of their risk analytics investments on underwriting, while
distribution sees the least capital. [Bar chart]. Retrieved from Accenture. (2012). Accenture risk management: 2012 risk
analytics study, insights for the insurance industry. [pdf]. doi: 12-3035 / 02-5176
Accenture. (2013). The digital insurer: achieving payback in insurance analytics. [pdf]. Retrieved from http://www.accenture.com/us-
en/Pages/insight-payback-insurance-analytics.aspx
American Institute for Chartered Property Casualty Underwriters/Insurance Institute of America. (2007). Predictive analytics white
paper. [pdf]. Retrieved from http://www.theinstitutes.org/doc/predictivemodelingwhitepaper.pdf
Ernst & Young. (2013). Advanced analytics for insurance. [pdf]. Retrieved from
http://www.ey.com/Publication/vwLUAssets/Advanced_analytics_for_insurance/$FILE/Adv-
analytics_insurance_AUNZ00000335.pdf
Gartner. (2013). Precision is the future of analytics. [pdf]. Retrieved from https://www.gartner.com/doc/2332716/precision-future-
analytics
Gartner. (2013). Use big data analytics to solve fraud and security problems. [pdf]. Retrieved from
https://www.gartner.com/doc/2397715
IBM. (2011). Analytics: the widening divide. [pdf]. Retrieved from http://www-935.ibm.com/services/us/gbs/thoughtleadership/ibv-
analytics-widening-divide.html
IBM. (2011). An information supply chain covers four segments of the information cycle: create, gather, package and provide and
consume. {Diagram]. Retrieved from IBM. (2011). Mass-produce insurance industry insight through business
analytics and optimization. [pdf]. Retrieved from
http://public.dhe.ibm.com/common/ssi/ecm/en/niw03006usen/NIW03006USEN.PDF
IBM. (2013). Harnessing the power of big data and analytics for insurance. [pdf]. Retrieved from
http://public.dhe.ibm.com/common/ssi/ecm/en/imw14672usen/IMW14672USEN.PDF
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21. IBM. (2013). Smarter analytics for better business outcomes. [pdf]. Retrieved from http://www-01.ibm.com/common/ssi/cgi-
bin/ssialias?infotype=PM&subtype=BR&htmlfid=YTB03064USEN
IBM. (2013). Business analytics for insurance. [pdf]. Retrieved from http://www-
05.ibm.com/cz/businesstalks/pdf/wp_business_analytics_for_insurance.pdf
International Risk Management Institute. (2014). Retrieved from http://www.irmi.com/
Ordnance Survey. (2012). The top-three concerns: recession, resources and policy inception. [Table]. Retrieved from Ordnance
Survey. (2012). Insurance fraud 2012: on the rise opportunistic and online. [pdf]. Retrieved from
http://www.insurancehound.co.uk/abstract/insurance-fraud-2012-rise-opportunistic-online-14539
Ordnance Survey. (2012). Insurance fraud 2012: on the rise, opportunistic and online. [pdf]. Retrieved from
http://www.insurancehound.co.uk/abstract/insurance-fraud-2012-rise-opportunistic-online-14539
Ordnance Survey. (2013). Respondents. [Bar chart] Retrieved from Ordnance Survey. (2013) The big data rush: how data
analytics can yield underwriting gold. [pdf]. Retrieved from http://events.marketforce.eu.com/big-data-
underwriting-report-email
Standish, J. (2012). Leveraging best practices with advanced analytics – making the right decisions in fraud investigations. [blog].
Retrieved from http://www.johnstandishconsultinggroup.com/JohnStandishConsultingGroup.com/Blog/Blog.html
Strategy Meets Action. (2012). What does big data really mean for insurers?. [pdf]. Retrieved from
https://strategymeetsaction.com/our-research/
Strategy Meets Action. (2012). Analytics domains and opportunities in insurance. [Diagram]. Retrieved from Strategy Meets
Action. (2012). What does big data really mean for insurers?. [pdf]. Retrieved from
https://strategymeetsaction.com/our-research/
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References