Analytics for P&C Insurance

8/13/2019 Analytics for P&C Insurance

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AnalyticsPotential value generation

P&C insurance

Gregg Barrett


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Executive Summary

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 willtouch on the general challenges confronting the property and casualty insurance industry, aswell as the challenges and lessons learnt from early adopters of business intelligence. In theface 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 atthe issue of fraud as it pertains to the industry and some of the metrics that are influenced byit.

The presentation draws extensively, and focuses on, the work and viewpoints from industryparticipants including; Accenture, IBM, Ernst & Young, Strategy Meets Action, OrdnanceSurvey, 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 thepresentation.

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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 gett ing 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 todays reality. Carriers were s upposed tohave 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.


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Definition: analytics

Analytics: The use of data and related insights developed through appliedanalytics 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 selectionbecause their market will be limited to a subsection for the general population that has worse-than-averageloss 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 andincreased competition has put immense pressure on insurers. Rapid integration of technology and life hascreated a proliferation of data, presenting unprecedented opportunities to use advanced analytics toleverage new informationabout 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 toaugment and enrich customer insights, financial management, risk assessment, and day-to-day operationsmean 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 andsophistication in predictive analytics wil l 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 provideand consume. Copyright 2011 by IBM Corporation. Reprinted with permission.


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Analytics domains in insurance

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Figure 3. Analytics Domains and Opportunities in Insurance . Copyright 2012 by Strategy Meets Action. Reprintedwith permission.


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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 newbusiness.

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 andancillary 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 reduceclaims fraud.

Performance management.

Combining what-if analytics, visualization and unstructured data, insurance carriers can develop easy-to-understand, actionableinsights by role in order to make optimal use of scarce and expensive human capital. In these and other areas, analytics conferson insurers the ability to improve underwriting, claims and distribution outcomes.


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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 ratingfactor. Loss costs for drivers with the highest-risk driving behavior are approximately two-and-a-half times thecosts for drivers with the lowest-risk behavior. These results suggest that car insurance rates could be far morepersonalized 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 onthe 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 insuranceclaims faster in order to zero in quickly on suspected fraud and speed up the settlement ofvalid claims.

With IBM predictive analytics, Infinity was able to:

Double the accuracy of fraudulent claim identification and accelerate the referral ofsuspicious claims to company investigators.

Improve customer satisfaction and retention by paying legitimate claims faster, contributingto 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 totalrepudiations 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 organisations 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 byOrdnance Survey. Reprinted with permission.

Table 1

The top-three concerns: recession, resources and policy inception


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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 toreduce 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.


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Fraud metrics

Fraud impacts

o Loss ratioCalculated as incurred losses to earned premiums expressed as a percentage (InternationalRisk Management Institute, 2014)

o Expense ratioCalculated as the percentage of premium used to pay all the costs of acquiring, writing, andservicing insurance and reinsurance (International Risk Management Institute, 2014)

o Combined ratioCalculated as the sum of two ratios, one calculated by dividing incurred losses plus lossadjustment 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 (InternationalRisk Management Institute, 2014)

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Conclusion

It is my recommendation that an analytics project covering claims managementand fraud be a priority. As shown with case studies examples of other carriers, thedata and technology toolsets are available, tried and tested, and the returns areasymmetrical - substantial rewards with little risk. Successfully applying analytics tothese areas will result in favourable improvements in the loss ratio, expense ratio

and combined ratio.

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ReferencesAccenture. (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, whiledistribution sees the least capital. [Bar chart]. Retrieved from Accenture. (2012). Accenture risk management: 2012 riskanalytics 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 whitepaper. [pdf]. Retrieved from http://www.theinstitutes.org/doc/predictivemodelingwhitepaper.pdf

Ernst & Young. (2013). Advanced analytics for insurance. [pdf]. Retrieved fromhttp://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 fromhttps://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 andconsume. {Diagram]. Retrieved from IBM. (2011). Mass-produce insurance industry insight through businessanalytics and optimization. [pdf]. Retrieved fromhttp://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 fromhttp://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 OrdnanceSurvey. (2012). Insurance fraud 2012: on the rise opportunistic and online. [pdf]. Retrieved fromhttp://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 fromhttp://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 dataanalytics 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 analyticsmaking 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 fromhttps://strategymeetsaction.com/our-research/

Strategy Meets Action. (2012). Analytics domains and opportunit ies in insurance. [Diagram]. Retrieved from Strategy MeetsAction. (2012). What does big data really mean for insurers?. [pdf]. Retrieved fromhttps://strategymeetsaction.com/our-research/

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References

Analytics for P&C Insurance

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