Predictive Analytics The Insurance Industry s New Focus for Greater Profitability

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3 Predictive Analytics The Insurance Industry s New Focus for Greater Profitability Today s Speakers Karlyn Carnahan Research Principal, Novarica Scott Horwitz Sr. Director, Insurance, FICO Ed Pulkstenis, CPCU EVP, Commercial Lines, Tower Group Companies Lamont D. Boyd, CPCU Director, Insurance Scores & Analytics, FICO (Moderator)

4 Predictive Analytics The Insurance Industry s New Focus for Greater Profitability Seminar Objectives Highlighting the continuing growth in predictive analytics within the insurance industry Providing key insights to enhance insurer profitability through predictive analytics Recognizing the value of generating immediate ROI by focusing on key decision components Identifying a process for successful implementation of predictive analytics in decision making

5 Big Data and Analytics in Insurance Presenter: Karlyn Carnahan Principal

6 Age of Data Super-Abundance Old Data Is Moving Faster Government data direct and through aggregators MVR Census BLS Local tax information Police/FBI data Medical data MIB Prescriptions Business data D&B, Lexis-Nexis, etc. New Data Is Proliferating Credit Consumer databases Social networking analysis Geographic information services Satellite photos Internal Data Is More Accessible and Better Database technology Data warehousing Enterprise Data Standards SOA

7 Third-Party Data Is Increasingly Used to Enable Key Capabilities Across All Business Processes Product Development & Maintenance Sales, Distribution Mgmt Marketing Policy Underwriting Claims Analytics Processing Consumer Business Policy, Claims Geographic, Geospatial, Peril Demographic Property Health Consumer Business Policy, Claims Demographic Competitive US Data Services for Insurers 2012 (Q1) Consumer Business Policy, Claims Geographic, Geospatial, Peril Demographic Competitive Consumer Business Policy, Claims Social Media Geographic, Geospatial, Peril Demographic Property Health Consumer Business Policy, Claims Property Health Policy, Claims Social Media Geographic, Geospatial, Peril Property Health Consumer Business Policy, Claims Social Media Geographic, Geospatial, Peril Demographic Property Health Competitive USPS Acxiom Social Intelligence Dun & Bradstreet RiskMeter INSURANCE CORE SYSTEMS MAP 2012 ISO/Verisk Primary Source Polk Distrib. Mgmt. BI: Business Intelligence: Reporting Repository Spec. Document Management & Workflow/BPM Comp. LexisNexis Consumer/ Business Pitney Bowes Business Insight Neustar Underwriter Core Claims System Agent Workbench Portal Rating Core Policy Rein. Engine Admin System Mgmt Customer CRM Billing Portal Document Creation Disbursements General Ledger Marshall & Swift/Boeckh Merkle Property Health Geographic/ Geocoding/ Peril Social Media Milliman MIB Group Melissa Data Several Major Data Categories 7

8 BI and Analytics Among the Highest Priority Projects For Insurers of All Sizes and Sectors for 2013 BI/Data Among Insurers Top-Three Business Capability Priorities for 2013 LH Insurers Under $1Bn (n=13) LH Insurers Over $1 Bn (n=11) PC Insurers Under $1 Bn (n=57) PC Insurers Over $1 Bn (n=21) 23% 8% 18% 16% 24% 18% 25% 18% 19% 14% 0% 20% 40% 60% Top Second Third 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Self-Assessment of BI Capabilities 24% 67% 5% 23% 18% 37% 55% 31% 23% 8% 25% 23% 27% 5% 5% 9% 8% PC Insurers Over $1 Bn (n=21) PC Insurers Under $1 Bn (n=57) LH Insurers Over $1 Bn (n=11) LH Insurers Under $1Bn (n=13) Very Strong Strong Acceptable Poor Very Poor 8

9 Where Are Carriers Using Analytics Today? Usage of analytics in actuarial modeling and risk analysis, pricing and product development is very high; operational analytics are less common. Larger insurers are more advanced than smaller ones. Areas of Analytics Usage by Insurers (n=86) Actuarial Modeling and Risk Analysis Pricing Producer Management Product Development Agent Compensation Lead development/ Cross Selling Claims Triage Workload Optimization Analysis Underwriter Assignment Percentage of Insurers in Sample Group 71% 24% 56% 27% 6% 34% 33% 35% 40% 10% 10% 17% 40% 6% 12% 9% 8% 31% 38% 34% 21% 21% 19% 5% 37% 6% Used extensively Used Planning to use within 12 months

10 Increased Use of Analytics Has Led to Increased Use of Predictive Models Predictive Models Usage by Insurers (n=86) Percentage of Insurers in Sample Group Underwriting risk score 29% 31% 12% Profitability models 26% 43% 12% Financial projections 24% 36% 9% Stochastic modeling 17% 17% 9% Claims severity score 12% 24% 21% Claims fraud score 10% 19% 22% Used extensively Used Planning to use within 12 months

11 Where Is Predictive Analytics Being Used In Insurance? Marketing Conversion, website optimization Producer management and compensation Lead development/cross selling Underwriting Risk analysis Pricing Premium audit Product/rate development Claims Claims triage, adjuster assignment Subrogation/litigation management, settlement Frequency and severity prediction Loss reserving Fraud (c) 2013 Novarica, a division of Novantas LLC. Presented by Martina Conlon (mconlon@novarica.com), exclusively for attendees. 11

12 Big Data Big data are typically characterized by: VOLUME VELOCITY VARIETY Big data are internal and external data, structured and unstructured, collected in enormous volumes at a rapid velocity. Big data are sometimes defined as data that are too large to be processed by traditional technology. Others define big data as Anything that doesn t fit in Excel

13 Examples of Big Data Sources and Types Audio data. Video data. Geospatial data. Internet clickstreams. Sensor data (including temperature, movement, speed, and other measures that are collected and recorded over time). Social media content data, images, recordings and other electronic content published on blogs, microblogs, social networking sites or usergenerated content websites, such as Wikipedia or YouTube. Social media content may include tweets, social network photographs and updates, online videos, online product reviews, and much more. Telematics or other behavioral data.

14 Usage Rates of Most Big Data Elements Are Still Quite Low, With the Exception of Geospatial Data Expected Data Source Usage by Insurers (n=86) Geospatial data Internet clickstreams Audio data (e.g. voice recordings) Historical stock market data Social-media content Mobile data/geospatial information Telematic data Sensor data Video data (e.g. digital video) Percentage of Insurers in Sample Group 29% 14% 12% 12% 10% 6% 8% 13% 9% 8% 5% 3% 2% 12% 12% 10% 13% 21% 12% 14% 2% 3% 3% 9% Already Using This Definitely Probably

15 What Are the Opportunities With Big Data? BETTER CUSTOMER ENGAGEMENT Enriched context around risks for Marketing, Underwriting and Pricing Early risk assessment from public information Early interest indications from clickstream and online behavior Distance to coast, other geospatial information Weather experience and predictions Telematics Video and satellite pictures Social media insights on policyholders (personal and commercial) Enriched context around claims and claimants Social media profiling and network analysis Detailed event data from telematics devices and home-monitoring systems Insights from analysis of the trends, anomalies and dependencies that can be used for Product design, rates, coverages, underwriting rules and appetite UW predictive models Claims/fraud predictive models Brand protection, marketing opportunities and sentiment analysis

16 What s Emerging in Big Data and Analytics? Even more predictive models Increased use of mobile devices by a more mobile workforce mobile dashboards and business intelligence Increased use of internal and external data to develop synthetic data surrogate credit scores, fraud indicators Real-time analytics, event-based processing???? More BIG DATA telematics, Internet clickstreams, sensor data, log files, mobile data rich with geospatial information, and social network comments Social media data in analytics, social media reporting service or a social media score?

17 Big Data and Analytics Challenges Significant up-front cost of specialized hardware and data. Limited access and high cost of analysis resources Can you hire a team of data scientists? (Data scientist = Quant skills + IT skills + Domain knowledge) Building the business case Will you see benefit? How do you measure it? Making it a priority How can this compete with policy admin replacements when it takes 2 years to get a product change to market? Misplaced sponsorship or advocacy IT enterprise architect or data architect.

18 The Combination of Big Data and Analytics Is Potentially Transformative for the Insurance Industry Improved customer engagement in an increasingly competitive marketplace Move risk selection forward from underwriting to marketing Streamline application process by asking fewer questions Multivariate constellation rating and predictive analytics Optimize workflow and leverage staff more effectively Evaluate claim early to maximize workflow and fraud detection

19 What Are You? Analytically challenged Areas of weakness may include insufficient/poor quality data (lack of governance/qa), weak information value chain (capturing, aggregating, analyzing, disseminating), lack of collaboration, no burning platform (gotten this far without using analytics). Analytical practitioners Just good enough data, focus on operational improvement rather than innovation, fragmented analytics ecosystem. Analytical innovators Top-down analytical culture, with analytics core to business strategy, more champions of analytics, more open to new ways of thinking. Analytical innovators collect more data (higher % collected one petabyte or more than other two segments), use much more of their data, and use analytics more for increasing their understanding of customers to make real-time decisions and to identify new markets (strategic).

20 Karlyn Carnahan Principal

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23 Leveraging Big Data FICO Credit-Based Insurance Scores Developed and introduced in 1993 ~95% of all auto and home insurance underwriting and pricing decisions consider credit-based insurance scores today FICO Falcon Fraud Manager over 20 years of success 2.5 billion cards worldwide More than 1 petabyte processed annually FICO Insurance Fraud Manager Provider profiles 100s of millions of claim lines FICO Has Long History

24 Achieving a View Multiple Databases P&C Policies Product Holdings Claims History Multiple Systems Pension Life Health Policies Bureau Data 360 º Customer View HR Suppliers Industry Data Finance

25 Predictive Analytics Increased Dimensionality With Big Data Dynamic Profiles Quotation Database Time to Report Claim Policyholder Details Claim Master Derive Powerful Variables Age of Car Residential Status Predictive Models Scores and Reasons Payments External Data Type of Accident Variable N

26 % Losses Modeler s Dilemma 100% Test Performance 50% Complex model Highly predictive in the lab Can t deploy Simple model Easy to deploy Lacks predictive power % 100% % Premium

27 Black Box Solution Big Data Can Lead to Increased Complexity

28 Glass Box Solution Strong Predictions With Operational Consistency All Prior Claims No Prior Claims Age < 40 Age >= 40 Score weights for char #1 Score weights for char # Score weights for char #1 3 4 Scorecard #1 Scorecard #2 Scorecard #3 Score Scorecard #1 if Policyholder with prior claims Scorecard #2 if no prior claims and age < 40 Scorecard #3 if no prior claims and age 40 Easy to understand, easy to engineer

29 Bias/Variance Dilemma Score Generated by a Model With Limited Data Score Generated by a Model With Big Data High Bias Inaccurate Predictions High Variance Inaccurate Predictions

30 Creating Model Diversity Key to Variance Reduction Train Multiple Models and Use Combinations To Take Advantage of Higher Dimensionality Model 1 Model 2 Model 3 Model N

31 Credit-based Insurance Score Big Data Credit and Property Inspection Data Improved Risk Assessments Property PredictR Score SCORE RANGE SCORE RANGE Lowest 20% Medium Low 20% Medium 20% Medium High 20% Highest 20% Total Lowest Scoring 20% Consistently Poor Risks 1.48 Medium Low 20% Medium 20% Medium High 20% Highest Scoring 20% Consistently Good Risks 0.56 Missing Credit Score Total Poor Risks Medium Risks Good Risks

32 Risk Score Risk Score Marketing Dual Objectives Tradeoff Metrics Adjust for risk Low Low Traditional Response Score High Low Low Risk-Adjusted Response Score High High High New training algorithm: multiple objectives Here, used to adjust a response score for risk More generally, can adjust any score for a secondary objective, subject to performance bounds on the primary objective

33 Modeling Approach Domain Knowledge Always Adds Value Data Superior Scores/Predictions Machine Learning Expert Models Informed by Data and Expertise Domain Knowledge

34 Analytics as Part of a Larger Communication Strategy 1 Collect and Apply Customer Transaction Information 5 Deliver Personal Offers/Treatments Transaction Data Demographic Info Response Data Customized Web Content Tara Transaction Data Demographic Info 2 3 Apply Analytics Apply Business Rules Personalized Offers by Mobile Device Lauren 4 Apply Optimization Transaction Data Demographic Info Online Behavior Survey Data Personalized Offers by Jake

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36 Predictive Analytics: Practical Applications & Proven Results Ed Pulkstenis EVP, Commercial Lines & Corporate Underwriting Tower Group Companies

37 This is not new

38 When it works.

39 When it works

40 And then it doesn t

41 In the insurance world 1 Best Risk X X X X X X X X X X X X Company A Worst Risk Company B

42 In the insurance world Hailstorm locations = bad workers compensation (WC) results. Hailstorms happen in the Midwest. WC results are bad in the Midwest. Midwest has poor management. AHA!

43 Four Simple (Yet Critical) Rules to Remember Don t let analytics intelligence replace common sense. (You need both.) Don t trust just your own observations. Analytic conclusions are only as good as the inputs internal and external. Respect insurance as a people business: company personnel, agents, customers it s possible to win the battle but lose the war.

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