Solace P&C Analytics

Potential value generation

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by Gregg Barrett

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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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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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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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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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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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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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.

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.

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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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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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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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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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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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

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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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.

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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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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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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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