201206 iasa session408-applied-analytics

June 2012 1

Applied Analytics: ConvertingMountains of Data into Nuggets of GoldSession 408 Tuesday June 5, 2012 11 AM

Session ObjectivesLearn how some insurers have found creative new ways tomake use of analytics, with the goal of turning information intomanagement action.Hear one carrier’s analytics case study and learn how to usean analytics roadmap to effectively assess and capture thebenefits of analytics company-wide.After attending this session, audience members will be able to:§ Describe a roadmap for applying analytics§ Evaluate the suitability of analytics for various functional areas§ Describe how one carrier has used analytics and related technologies to improve business performance June 2012 3

Top Line Benefits of AnalyticsBusiness analytics enable organizations to be able to:§ Gain deeper, more relevant business insights to inform decisions§ Bring predictive analysis and regression modeling to entire organization§ Use analytics to identify and determine options for industry challenges§ Effectively and proactively manage risks§ Strengthen data governance at each level of the organization§ Reduce costs through more accurate, data-driven decision-making§ Use analytic capabilities and outcomes for change management efforts§ Create a culture that thrives on fact-based decisions versus “gut”Impact on decision-making is one of the key reasons…… June 2012 4

Top Reasons for Adopting Analytics June 2012 5

Survey Says….AKA “gut feel” Future Projections (Predictive) 2% 41% 24% 33% Historical Data 2% 25% 36% 36% Collaborative Consensus 7% 28% 43% 22% Group Dynamics 2% 34% 39% 24% 1% 82% 60% Experience 7% 32% 55% 5% Intuition 5% 31% 38% 25% 0% 20% 40% 60% 80% 100% Not at all Some Typical/Common Almost Always Exclusively June 2012 Robert E Nolan Company Executive Survey, 2011 6

Analytical Companies Perform Better 27% year over year growth A difference of 26% in year on year growth 1% year over year growth June 2012 7

Yet Companies Struggle to ImplementMost frequent reasons companies struggle with analytic initiatives:• Too much management, not enough leadership• Limited support and buy-in at multiple levels within the organization• No guiding purpose or vision for people to rally around• Overemphasis on technology implementation/success criteria• Business benefits are too fuzzy to articulate and communicate clearly• No consistent communication or messaging to stakeholders• Poor identification of stakeholders and influencing factors• Compensation structures and incentives not alignedSurvey Comments on Barriers to Growth in Use of Analytics“Resistance comes from most experienced, those requiring 100% accuracy”“Access to critical data that is not captured in the system but is on paper”“Getting away from tribalism, managing by anecdote and subjective decisions”“Availability of resources and the money necessary to do it right”“Data is spread all over and difficult to integrate or consolidate”“Privacy will become a major issue as external data sources drive decisions” June 2012 Robert E Nolan Company Executive Survey, 2011 8

And Opinions Vary Greatly (2011 Nolan Analytics Survey Comments)“The importance placed on analytics will grow, however there will be adisproportionate reliance placed on results, until management learns thatgarbage in/garbage out continues to cast its shadow.““It really doesn’t matter as most data currently produced comprises thebasis for most uses necessary. Advanced techniques do not thereforeproduce ‘advanced’ data - the numbers are the numbers no matter howproduced. Indeed, give me a room full of ladies in green eyeshadesand Marchant calculators and maybe a punch card reader or two and Icould be perfectly happy with managing the business, no matter howcomplex.““Those companies that do not embrace technology and analytics willbe left behind in the dust of those companies that do. “

Retrospective versus Predictive June 2012 10

Case Study A: Prospect Scoring Scoring of prospects based on conversion and Psycho- value, marketing strategy developed to match graphic Data Potential Value Text High value, High value, High value, High Low Medium High Data conversion, conversion, conversion, 2nd Priority Top Priority Top priority Predictive Potential Medium Good value, Good value, Good value,Web Analysis Future Low Medium HighLog and Value of conversion, conversion, conversion,Data Low Priority 2nd Priority Top Priority Modeling Customer Low value, Low value, Low value, Low Medium High Low Survey conversion, conversion, conversion, Data Low Priority Low Priority 2nd Priority Purchased Low Medium High Data Propensity to Convert June 2012 11

Case Study B: Agency Management60% of customers would switch carriers if advised to do so by their agent.(Source: JD Power & Associates)33%+ of agents are likely to change insurance carriers.(Source: National Underwriter and Deloitte)Insurers that better manage their agents achieve competitive advantage.§ New customers have high acquisition expense, retaining one is more profitable.§ New agents have high acquisition expenses and pose a greater risk of inferior retention rates, resulting in lower profits.§ Monitoring effectiveness of agents provide early warning that an agent may be about to leave, triggering action and market differentiation.§ Predictive scorecards tie traditional features like traffic lights and speedometers to powerful analytics. § Dashboard visuals provided at-a-glance access to the current status of new KPIs, with automatic alerts for underperforming objectives and strategies.Case Study B implemented an agency dashboard based on new KPI’s thatwere modeled with a predictive analytics tool. June 2012 12

Case Study B: Agency Management Shift away from traditional sales performance metrics like Premium Revenue. New KPI: Agent Profitability net of cost to serve –calls, e-mails, supplies, etc. June 2012 13

Case Study C: Loss based Pricing Territory average loss ratios generate prices that are too high for some and too low for others. $812.50 Detailed risk analytics $438.00 generate more accurate loss cost estimates by discrete $1187.00 segments of business. Result: More equitable and competitive risk adjusted pricing. ISO Price Analyzer Tool used for graphics June 2012 14

Case Study D: Retention StrategiesStep 1: Determine Life time Value Post Purchase Activity – Increases in Future predictive value Value over time as behavioral patterns develop Predictive Analysis Customer behavior shifts focus from Time of Purchase current to future value Demographics - Loses predictive Current value over time Value as relevance is superseded by inforce behaviors June 2012 15

Case Study D: Retention StrategiesStep 2: Predict Potential LapseSource of Business influenceslapse tendencies based onchannel behaviors Predictive Analysis – ModelTransaction behavior Channel andinfluences lapse tendencies Consumerbased on consumer behaviors Behaviors June 2012 16

Case Study D: Retention StrategiesStep 3: Develop Strategy Matrix Match effort to risk and value – • High value low risk gets medium effort, save money on retaining low risk customers • Low value customers get low cost efforts across the board • Targeted high efforts on high value / high risk June 2012 17

Case Study E: Claims Fraud About 10% of all insurance claims are fraudulent. Annual fraud losses for P&C industry total $30B in US alone. § Need to detect unknown patterns of financial fraud. § Keep track of new fraud schemes. § Unsure exactly what to look for. Rules: Captures fraud on known patterns previously used Ex: Two claims in different time zones within short window Anomaly Detection: Detect unknown patterns (ind & aggr) Ex: Statistics (mean, std dev, uni/multivariates, regression) Advanced Analytics: Detect complex patterns Ex: Knowledge discovery, data mining, predictive assessment Social Network Analytics: Determine associative links Ex: Knowledge discovery via associative link analysis (entity map) June 2012 SAS Institute 18

Automated Fraud Detection Points Prioritized investigation SIU Re-estimate duration Focus on organized fraud Reassess loss reserving Minimize claim padding Prioritize resources Fraudulent rescoring Reduce false positives Review litigation propensity Fraud Referrals Fraud Referrals FNOL Assign Evaluate Update Close Claim Claim Claim Claim Fast Track Claim Cross-sell options for satisfied customer Negotiate / Customer retention programPredict duration Initiate ServicesForecast loss reservesOptimize fast track claimsPrioritize resources Identify salvage and subrogationFraudulent scoring Initiate opportunitiesLitigation propensity Indicate deviations from similar Settlement claims June 2012 Reports on claims overrides19

Other Brief Claims ExamplesOptimized Claims Adjudication process.§ Using data mining to cluster and group claims by loss characteristics (such as loss type, location and time of loss, etc.).§ Claims scored, prioritized and assigned per experience and loss type.§ Higher quality, more consistent, and faster claims handling.Adjuster Effectiveness Measurement.§ Adjusters typically evaluated based on an open/closed claims ratio.§ Analytics create key performance indicator (KPI) reports based on customer satisfaction, overridden settlements and other relevant metrics.Claims involving attorneys often 2X settlement and expenses.§ Analytics help determine which claims are likely to result in litigation.§ Assign to senior adjusters to settle sooner and for lower amounts. June 2012 20

One More Claims ExampleAnalytics help shorten the claims cycle times. § Claims cost 40% more if FNOL is delayed by 4 days. § Auto accidents take average of 16.2 days to repair and return. § Significant expense savings on rental cars, etc. for auto repairs. Duration and Customer Satisfaction are directly correlated. 854 % of Claimants 828 Overall CSI Index 772 Satisfaction going down CSI of 854 to 828 to 772 37% 36% 27% JD Power, 2007 1 wk or less 8-14 2007 JD Power, days Over 2 wksAnalytics drive higher Customer Satisfaction and Lower Costs June 2012 21

3 Guidelines to Implementing AnalyticsAll-executive panel agreed on three key guiding principles:1. Have an executive sponsored roadmap that clearly outlines. § What resources will be needed for how long, § Where and when predictive analytics will be used, § Which tools will be used, and § How will success be measured.2. Use data that is comprehensive, accurate, and current. § Not necessarily 100%, some have used only 70%. Must be representative.3. Staff with talented and engaged people. § Completely understand business problem and are proficient with analytics. § Every person does not have to meet both qualifications; a team can be used with some experts on the business and others experts on analytics. June 2012 22

Closing ThoughtsWhen it comes to leveraging customer analytics, rememberthis well-known proverb: The best time to plant a tree was 20 years ago. The second best time is today! June 2012 23

Are You Motivated? June 2012 24

Questions THANK YOU!! Robert E. Nolan Company Management Consultants www.renolan.com Steven M. Callahan, CMC® Practice Director www.linkedin.com/in/stevenmcallahan June 2012 25

201206 iasa session408-applied-analytics

by Steven Callahan on Jun 23, 2012

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