Karlyn Carnahan, today a principal in Novarica's insurance practice,recalls that when she was a senior executive at an insurancecarrier 10 years ago, “business intelligence” meant getting a boxof reports.

“I wouldn't get another update for at least a month,” she says.“It was difficult to match policy to claims data, and if I needed aunique report, I'd have to commission one from IT,” she says.

But waiting until next month is no longer an option in today'sinsurance marketplace. “Insurance companies need to useunderwriting and claims intelligence to gain advantage in themarket,” says Michael Ferguson, managing director of UK-basedIntelligentBusiness Strategies. “They obviously are keen on keeping theircosts down. That means they have to avoid insuring high-risk[accounts], and they have to make sure that rating is correct.”

Expanding BI capability is a high priority for insurers,particularly in P&C where 43 percent of carriers report BI is atop-three project, and 20 percent are focused on predictiveanalytics (see project priorities chart, p. 14).

“Technology today allows you to do real-time, on-demandreporting. Many insurers have access to better data environments,with more accessible and better internal data. And there is a tonof third-party data you can append,” Carnahan adds. “This has ledto the use of some very sophisticated algorithms and scoring inunderwriting, claims, and other areas.”

It also has led to carriers' increased willingness to provideusers greater control of BI. “In the past, access to data tended tobe tightly controlled, mainly because it required fairlysophisticated knowledge to define an ad hoc report and scarcesystem resources to run,” Carnahan says. “Today, carriers oftenhave user-friendly reporting tools in place that make it easy todefine a report and drill down on the information needed.”

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IN CONTROL

NationwideInsurance realized it had a wealth of data on interactions withcustomers, including records of compliments, questions, andcomplaints. What it didn't have was a good way to put thatinformation in the hands of people who could make a difference.

“We realized we weren't doing a whole lot with that [customercontact] data, so we wanted to create a system that would offerreal-time access to business intelligence and analytics to all ourusers and to provide usable and measurable data in an easy-to-use,Web-based environment,” says George Armstrong, Nationwide'sdirector of corporate customer relations.

Nationwide captures customer feedback from any channel–phonecalls, Internet transactions, postal mail, and social media–withina centralized customer contact database. Leveraging an investmentin a BI platform already in use in the enterprise (which Nationwidedeclined to name), the carrier built a customer experiencemanagement system it deployed in
January 2010.

Users log on to the Web-based platform, which presentsdashboards customized to their areas of responsibility. Problemareas are flagged, and users can drill down into those areas toidentify the root cause and take action. Department-leveldashboards roll up information and activity to managers who canensure customer service issues are being handled by individualstaff and create service-improvement action plans.

User-group testing of the system has Armstrong optimistic aboutthe benefit to customer service. “We already can see positiveimpact based on the fact we have information out there,” he says.“It gets complaints and inquiries in front of the rightpeople.”

Nationwide is one of many carriers that are targetingperformance improvement by putting actionable intelligence in thehands of the people directly responsible for that performance inunderwriting, claims, and other areas. Rather than requiringbusiness staff to approach IT for every modification made topredefined management reports, these companies have worked not onlyto provide information to people when and where they need it butalso to allow users control over BI tools themselves in order toperform iterative analyses and drill down into underlying data.

However, opening up broader access to BI creates some newchallenges and risks for insurers as sensitive or proprietary datais freed from IT lock-down. Managing those risks requiresidentifying best practices around pushing control to businessusers.

“The question insurers need to answer is who can see what andwho can do what,” Carnahan says.

That includes establishing permission-based access around whohas access to standard reporting vs. ad hoc reporting. “Insurerstend to limit ad hoc or 'deep drill' access to certain roles orcertain types of information,” Carnahan says. “Ideally, insurersshould establish different dashboards for different users so it'seasy for those users to get to the right level of data needed to dotheir jobs.”

For instance, claims representatives might have a dashboard thatenables them to manage their own caseloads. While it shows howtheir performance compares with departmental standards, thedashboard doesn't include caseload details for the entire team.Higher-level users don't have individual case detail automaticallyon their dashboards but can drill into that detail as needed toaddress hotspots.

Nationwide's customer experience management system provides thatlevel of user-based customization. “The tool has controls aroundwhat data can get pushed to individual users and how far thoseusers can drill into underlying data,” Armstrong says.

In addition to technology-based controls, carriers need toestablish business-based best practices around pushing additionalcontrol of BI. “Carriers need to be sure their operationalprocesses are in place for data security,” Carnahan says.

For example, there's no benefit to restricting a level of systemaccess for agents if a marketing rep turns around and provides thatinformation to the agent. Carriers need to provide training notjust on tools themselves but on broader issues of dataprotection.

BROADER BI

The trend of pushing BI to the business user is clearly seen inpredictive analytics, where underwriters, claims, and otherline-level staff use analytics tools for purposes ranging fromsupporting individual case decisions to monitoring the portfolio ofwork for which they are accountable.

Some definitions of BI encompass only the more traditionalfunctions of reporting and analysis, but Carnahan prefers a morecomprehensive view that includes predictive analytics, as well. “Iwould define BI as anything that helps you use data to analyzeinformation, make decisions, and analyze performance,” sheexplains. “That includes data mining, benchmarking, text mining,all the way up to predictive analytics.”

SocietyInsurance targeted more consistent decision-making and pricingof accounts when it put an analytics tool from Valen Technologiesin the hands of underwriters but quickly found the impact of the BIplatform extended much further.

“What surprised us was our underwriting staff has used the toolbeyond the individual risk pricing and selection,” says Rick Parks,Society's COO. “They've applied it to book-of-business reviews toanalyze how that book should be priced and why, compare how we haveit priced, and pro-ject what the results could have been. We'refinding some unexpected opportunities.”

Society contracted with Valen in 2008 and put the tool intoproduction in August 2009, starting with new business in workers'compensation. “We were surprisingly pleased with the amount andquality of our data, but due to resource limitations on our end, ittook us longer to have the model developed than we might haveliked,”
Parks says.

The SaaS-based system uses structured policy or application dataand automatically draws data from external sources, such as theBureau of Labor Statistics. Based on the analysis, underwriters arepresented with a pricing recommendation. How underwriting acts onthat recommendation is captured by Society's enterprise BI platformfor traditional reporting analysis.

“If the model recommends a certain price point and theunderwriter decides to go a different direction, we can analyze howthat turned out and either fine-tune the model or educate ourunderwriters,” says Parks.

A few months into the system, Society is on the path toachieving its objective of driving greater consistency into theunderwriting process. “We didn't want to roll out a hands-off modelto make an up or down decision, but it's given the underwriters apoint of reference for pricing,” Parks notes. “Particularly onreviews of unprofitable books, we've found the results prettystriking–situations where if we had turned the clock back andapplied the pricing the model had recommended, our results wouldhave broken even.”

Predictive analytics also is becoming a business necessity,continues Parks. “We believe at some point, our competitors will beusing this technology, and if we don't, we will be at a competitivedisadvantage,” he points out. “Particularly in the small accountmarket, accounts can look very similar but be substantiallydifferent. Putting BI in the hands of underwriters to help themfind those differences is the key to gaining a marketadvantage.”

Society plans to roll out the system to other lines of businessand processes. For instance, premium audit could develop a model todetermine what types of accounts would be most likely to generateadditional premium. “We continue to build up our base of datapoints and look for more areas of correlation,” says Parks.

Society sees the potential, as well, to better manage itsindependent agency force. “We think there's work that can be doneto analyze our agents more effectively and determine how differentagencies, or different tenures of agencies, perform,” Parks says.“Those are the types of analytics marketing people do now throughtraditional reporting, but we think there is upside to being moreprecise on how different data points impact each other.”

Agency management is a natural fit for analytics, assertsFerguson. “BI helps choose the right brokers to do business withand get a good channel going,” he says. “You need to direct yourefforts to brokers who are more profitable.”

Producer management was a key objective of Zurich, which has putBI in the hands of the users through SAS's business analyticsplatform. “Once we've built a predictive model, we look at aterritory and identify segments of a book we want to go after, thenwork with our direct market agents to target that business,” saysJoel Appelbaum, chief analytics officer of Zurich NorthAmerica's commercial programs and direct markets. “We'veoptimized our marketing by prioritizing it based on analytics.”

Underwriters use the system for individual risk decisionsupport. “We used to come up with an average class rate and somecharacteristics that would differentiate it. Now, we're looking atfuture projected profitability based on inferential statistics,”says Appelbaum, adding analytics has helped Zurich trimunderwriting expense.

“We can save money on ordering MVRs by predicting which accountsare likely to drive in more congested areas, based on neuralnetwork patterns we developed,” he says.

In addition, analyses can determine whether intervention or moreservice is needed to prevent problems from occurring. For instance,because Zurich has identified a correlation between an accountrelocating its business and a spike in claims, loss controlengineers can use the system to prioritize those accounts forreinspection.

Although users are provided the tool, they ultimately have thecontrol–and responsibility–of whether to use the intelligence itprovides or not. “We create the model and provide the KPIs to ourstaff, but you have to give your staff the latitude to run thebusiness,” Appelbaum says.

Zurich estimates a savings of $5 million annually fromincorporating predictive analytics into its overall BI effort,gained from both expense and loss reduction. “That's not alot–roughly one percent of our loss ratio–but every little bitcounts in this game, and we think as we implement more, we'll seeadditional savings,” Appelbaum says. “I also look how profitable weare in the pit of a soft market, which shows our discipline topricing and underwriting that is supported by analytics.”

BEYOND REPORTING

In today's economy, “a 101 [combined ratio] is not what it usedto be,” Carnahan says. “In order to generate a profit, insurersmust find ways for business users to leverage BI and truly improveperformance. And particularly today, when the insurance market isshrinking, it is imperative to generate efficiencies to get agreater return, improve underwriting results, retain and sell moreto customers, and get new customers.”

OlympusInsurance has minimal IT staff, outsourcing its policy andclaims administration systems and business processes. The companyrelies on its BI platform for performance management and to providean important control process between its own staff and itsoutsourcers.

“We want to focus our IT resources on product development andfinancial management rather than running the ad hoc queries weneed,” says Brad Burton, vice president of underwriting at Olympus.“We wanted a centralized source that both we and our outsourcerscould provide data to and that would deliver information to us onour desktop on a daily basis. We also wanted a resource that wouldbe available to managers whether they're in an office, at home, oron the road and that would provide comprehensive and consolidatedintelligence.”

Olympus uses a Web-based BI application from iPartners withaccess provided to everyone who is an operational manager. Olympusestablishes the KPIs for performance management on a corporatelevel, and users are provided the flexibility to conduct analysiswithin those parameters. For example, users can't change thedashboard and KPIs that are measured, but they can drill down thosemeasurements when problems arise rather than asking IT for moredata.

Burton illustrates this with a recent example from underwriting.“One of the marketing managers noticed some underwritingtransactions that seemed to be increasing in her area, including anincrease in cancellations for accounts that didn't meetunderwriting criteria,” he says. “Rather than contacting IT for aquery, waiting for that, then finding it didn't have theinformation she needed, she could drill down into the cancellationsthat were made, why they were processed, and what territory theywere for,” he says.

“With the iPartners system, the answer to this question could befigured out much more quickly than it otherwise would have. It's amore comprehensive way to view the data, with many more dataelements than you would get in a report,” Burton adds.

Olympus plans to add predictive analytics to its BI solution, adevelopment that will happen “sooner rather than later,” accordingto Burton.

“Our current environment allows us to gather and dissectinformation. We built the 'slicing and dicing' part upfront. Thenext piece is to take our book of business, internally analyze allthese data elements iPartners has supplied us, and create ananalytics model,” Burton says. “We want to get that information inthe hands of the sales force in real time to target business thatwill be profitable for us.”

CHALLENGES

Despite the proliferation of new tools, BI relies first andforemost on good data. “One of the challenges of setting up [ananalytics tool] is proper treatment of missing or poor data,” saysAppelbaum.

“There are many impediments to successful BI,” says Carnahan.“First is often insurers don't have an enterprise data model. Theydon't have consistent data across different applications, so theycan't pull data from across applications in a consistent way. Thatincludes basic problems such as the policy admin system using a'policy issued date' and the claims admin system using 'age ofpolicy' to reflect the same information.”

And despite the apparent wealth of data at a typical insurancecompany, many insurers remain information poor. “You will find thesituation where companies don't fully capture risk informationuntil a claim happens,” Ferguson says. “So, when it comes to BIsystems, an insurance company often is lightweight on insured riskdata. And in addition, if it gets a lot of its business viabrokers, it is lightweight on customer data.”

“Other challenges to creating value include difficulty makingchanges to the core systems in order to capture the right dataelements. Those systems often lack reporting capabilities, theylack ETL capacities, and insurers themselves often lack informationdistribution capabilities,” Carnahan says. “Additionally, it's veryeasy for users to get tricked by a false sense of precision withpredictive analytics. You have to remember with predictiveanalytics, it's a best guess–just because a claim scores high on afraud scale does not mean that claim is actually fraudulent.”

Unstructured data, where text mining approaches are needed toextract gems of intelligence from mountains of potentialinformation, continues to present challenges for insurers, too.“Insurers have to invest in data integration tools and some kind oftext mining in order to get the data out about insured risk, whichwould give them a lot more knowledge about claims patterns arisingfrom certain types of risks,” Ferguson says.

But for insurers that can solve these data challenges and put BIin the hands of business users, payback awaits.

“We're in a world where the customer is king, and we havecomparative rating sites everywhere. Being able to respondaccurately and in a time that suits the customer is important;however, companies also want to make sure they write the rightbusiness,” Ferguson says. “For those reasons, even in the sleepyworld of insurance, we will see more and more dependency
on BI.” TD