Value proposition of analytics in P&C insurance

A report on the value proposition

of analytics

Solace P&C

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 at Solace P&C. 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. Reprinted with permission.

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. Reprinted with permission.

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. Reprinted with permission.

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. Reprinted with permission.

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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)

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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)

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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.

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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.

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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 • 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

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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)

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.)

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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).

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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).

(Accenture, 2013, pg. 6) 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.

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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).

(Accenture, 2013, pg. 3) 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.

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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.

(Accenture, 2013, pg. 3) 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. Reprinted with permission.

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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, Solace P&C 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.

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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.

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