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201406 IASA: Analytics Maturity - Unlocking The Business Impact
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201406 IASA: Analytics Maturity - Unlocking The Business Impact

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Overview of how experienced insurers are finally unlocking the business value of analytics to strengthen financial results through improved underwriting, better pricing, agent enablement, enhanced …

Overview of how experienced insurers are finally unlocking the business value of analytics to strengthen financial results through improved underwriting, better pricing, agent enablement, enhanced risk management, and targeted cost reductions and how analytics maturity and a roadmap increases the odds of success.

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  • 1. IASA 86TH ANNUAL EDUCATIONAL CONFERENCE & BUSINESS SHOW Analytics Maturity: Unlocking the Business Impact of Analytics Session 102
  • 2. Analytics Maturity: Unlocking the Business Impact of Analytics Session Overview: § Analytics are being used to strengthen financial results through improved underwriting, better pricing, agent enablement, enhanced risk management, and targeted cost reductions. § Learn how experienced insurers are finally unlocking the business value of analytics by implementing an analytics maturity model. § Hear one carrier’s analytics case study. Session Objectives: § Describe an analytics maturity model § Identify analytics-enabled opportunities and ROI § Describe how one carrier has used analytics and related technologies to improve business performance
  • 3. Analytics: Using data to make smart decisions Data Historical Simulated Text Video, Images Audio §Data inputs §Reports and queries on data §Predictive models §Answers and confidence §Feedback and learning Decision point Possible outcomes 3 How are decisions made? How can they be better informed? How does business structure impact decision?
  • 4. The Analytics Hierarchy Extended from: Competing on Analytics, Davenport and Harris, 2007 Report Decide and Act Understand and Predict Collect and Ingest/Interpret Traditional Analytics New Data New Methods Standard Reporting Ad hoc Reporting Query/Drill Down Alerts Forecasting Simulation Predictive Modeling Decision Optimization Optimization w/uncertainty Adaptive Analysis Continual Analysis Unstructured text/video/audio Enterprise-wide adoption New extractions methods Learn
  • 5. New Data Sources + Fewer Boundaries = Greater Value Sourcesandtypesofdata New format or usage of data Structured or standardized Scope of decisionLow High Multi-modal demand forecasting Intent-to-buy trends Segmentation- based market impact estimates Price-based demand forecasting (own & competitors)Sales-based demand forecasting
  • 6. * Truthfulness, accuracy or precision, correctness Big Data in One Slide Volume Velocity Veracity*Variety Data at Rest Terabytes to exabytes of existing data to process Data in Motion Streaming data, milliseconds to seconds to respond Data in Many Forms Structured, unstructured, text, multimedia Data in Doubt Uncertainty due to data inconsistency & incompleteness, ambiguities, latency, deception, model approximations
  • 7. Big Data is Getting Bigger and More Diverse
  • 8. Uncertainty Arises from Many Sources Model Uncertainty Process Uncertainty Data Uncertainty John Smith John Smythe
  • 9. Key Applications of Analytics § Gain deeper, more relevant business insights to inform decisions § Bring predictive analysis & regression modeling to entire organization § Use analytics to identify and determine options for addressing 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 § Create a culture that thrives on fact-based decisions versus “gut” Analytics: A Cross-Functional Solution to Information Overload
  • 10. Leadership Decisions Moving To Data Driven Analytics: A Cross-Functional Solution to Information Overload
  • 11. Analytics Used Across Wider Variety of Areas Analytics: A Cross-Functional Solution to Information Overload
  • 12. Relative Adoption by LOB Analytics: A Cross- Functional Solution to Information Overload 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% Predictive Retrospective
  • 13. Source of Increasing Interest in Analytics
  • 14. Location Of Analytics Expertise Varies Widely ?
  • 15. Increase in Analytic Methods Being Used
  • 16. Analytics Progression PROACTIVE DECISIONS REACTIVE DECISIONS
  • 17. Maturity by Progressiveness
  • 18. Maturity by Focus
  • 19. Maturity by Stage Level Effectiveness
  • 20. Maturity by Level of Integration
  • 21. Maturity by Utilization Cycle
  • 22. Whatever Maturity Model is Used: Measure the Maturity Capability By Function
  • 23. The Analytics Capability Maturity Evolution
  • 24. Level 5 Analytics Requires Integration and Continuous Enhancement
  • 25. Analytics Team Effectiveness: Measure Using RATER Model
  • 26. Elements of the RATER Model The RATER* Model: 1. Reliability –the ability to provide the service you have promised consistently, accurately, and on time 2. Assurance –the knowledge, skills, and credibility of staff; and their ability to use this expertise to inspire trust and confidence 3. Tangible –high quality, or appearance of high quality in the physical aspects of service delivery. Includes documents, presentation, facilities and packaging 4. Empathy –the extent to which analytics area(s) adequately represent the concern and values of the functions and areas served 5. Responsiveness –the ability to provide effective answers and solutions quickly or within needed expectations *Source: Delivering Quality Service…, Zeithamlet al, 1990
  • 27. From Reporting to Innovation Analytics: A Cross-Functional Solution to Information Overload
  • 28. Leveraging the Foundations of Wisdom: The Financial Impact of Business Analytics (© IDC) IDC Research showed tremendous gains – Median ROI: Predictive: 145% NonPredictive 89% 30% 25% 20% 15% 10% 5% 0% 1-50% 51-100% 101-500% 501-1000% >1,000% More Informed Decisions Improves ROI Analytics: A Cross-Functional Solution to Information Overload
  • 29. Top Line Revenue is Improved As Well Carriers effectively using predictive analytics achieved: • 1% improvement in profit margin • 6% improvement in year on year customer retention Carriers not fully using predictive analytics: • Dropped 2% in profit margins • Decreased 1% in year on year customer retention Higher on the Capability Maturity Curve = Better Results: • Top 20% : 27% Year on Year Growth in Revenue • Middle 50% : 12% Year on Year Growth in Revenue • Bottom 30% : : 1% Year on Year Growth in Revenue
  • 30. Case Study: Agency Management 60% of customers would switch carriers if so advised by their agent. (Source: JD Power & Associates) 33%+ of agents are likely to change insurance carriers. (Source: National Underwriter and Deloitte) Insurers better manage their agents achieve competitive advantage. § 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. Implemented an agency dashboard based on new KPI’s that were modeled with a predictive analytics tool.
  • 31. Case Study: Retention Strategies Step 1: Determine Life time Value 31 Time of Purchase Demographics -Loses predictive value over time as relevance is superseded by inforce behaviors Customer behavior shifts focus from current to future value Predictive Analysis Current Value Future Value Post Purchase Activity – Increases in predictive value over time as behavioral patterns develop
  • 32. Case Study: Retention Strategies Step 2: Predict Potential Lapse Predictive Analysis – Model Channel and Consumer Behaviors Source of Business influences lapse tendencies based on channel behaviors Transaction behavior influences lapse tendencies per consumer behaviors
  • 33. Case Study: Loss based Pricing Result: More equitable and competitive risk adjusted pricing. $812.50 $1187.00 $438.00 Territory average loss ratios generate prices that are too high for some and too low for others. Detailed risk analytics generate more accurate loss cost estimates by discrete segments of business. ISO Price Analyzer Tool used for graphics
  • 34. Case Study: Claims Processing FNOL Evaluate Claim Close Claim Negotiate / Initiate Services Predict duration Forecast loss reserves Optimize fast track claims Prioritize resources Fraudulent scoring Litigation propensity Identify salvage and subrogation opportunities Indicate deviations Reports on overrides Initiate Settlement SIU Update Claim Fraud Referrals Fraud Referrals Re-estimate duration Reassess loss reserving Prioritize resources Fraudulent rescoring Review litigation propensity Cross-sell options for satisfied customer Customer retention Assign Claim Fast Track Claim Prioritized investigation Focus on organized fraud Minimize claim padding Reduce false positives
  • 35. Case Study: Claims Processing Optimized 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 by 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 metrics. Claims using 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.
  • 36. Case Study: Claims Fraud Red Flag Dashboard June 2012 36Courtesy of Attensity Analytics: A Cross-Functional Solution to Information Overload
  • 37. Case Study: Life Underwriting via App + Third Party Data Second child born last year High investment risk tolerance Lived in home 2 years Owns home Commuting distance 1 mile Reads Design and Travel Magazines Urban single cluster Premium bank card Good financial indicators Active lifestyle: Run, Bike, Tennis, Aerobics Health food choices Little to no television consumption Actively pursue for issuance of a preferred policy without requiring fluids or medical records. Use strong retention tactics.
  • 38. Case Study: Life Underwriting via App + Third Party Data Do not send offers. Do not pursue aggressive retention strategies. If applies, pursue additional medical records and tests. Current residence four years Lived in same hometown 15 years Currently renting Commuting distance 45 miles Works as administrative assistant Divorced with no children Foreclosure/bankruptcy indicators Avid book reader Fast food purchaser Purchases diet, weight loss equipment Walks for health High television consumption Low regional economic growth Light wine drinker
  • 39. Case Study: Life Underwriting Analytics and Non Intrusive Data Life UW using a GLM predictive model to assess risk: § Use info on app plus social data, No fluids or files § Integrate 3rd party publicly available information. In a test over 30,000 applicants: • Behavioral and lifestyle factors provided 37% of the risk assessment influence • These factors performed as well as additional, more intrusive medical tests and fluids. Third party marketing datasets used to develop predictive models: • Include over 3,000 fields of data, • Contain no PHI, • Are not subject to FCRA requirements, and • Do not require signature authority. The match rate with insured’s is typically around 95% based only on name and address.
  • 40. Sources of Third Party Data Pervasive Survey Data: • Self-reported information • Contains many lifestyle elements Basic demographics • Age, sex, # & ages of kids, marital status • Occupation categories, education level Financial information • Income, net worth, savings, investments • Home value, mortgage value, CC info Lifestyle data • Activity: Running, golf, tennis, biking, hiking • Inactivity: TV, PC’s, video games, casinos • Other: Diet, weight-loss, gardening, health foods, pets Rewards programs Magazines Email lists Websites Grocery store cards Book store cards Public records
  • 41. Life Underwriting Savings: Using 3rd Party Data versus Medical Data Deloitte Predictive Model for Life
  • 42. Workers Comp already has a track record of using Social Data
  • 43. Case Study: Social Analytics Customer Engagement Dashboard § Automatically monitor social conversations § Filter out irrelevant posts § Analyze posts to extract key insights § Engage customers with proactive outreach § Improve experience customers are having on the site § Improve brand image and emphasize business legitimacy
  • 44. Case Study: Social Analytics Conversation Sentiment Tracking Courtesy of Attensity
  • 45. Case Study: Social Analytics Website Sentiment by LOB Courtesy of Attensity
  • 46. Social Analytics: Overall Sentiment Ratings Dashboard
  • 47. Case Study: Social Analytics Competitive Sentiment Dashboard Courtesy of Attensity
  • 48. Yet Companies Struggle to Implement 48 Most 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 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 aligned
  • 49. Common Barriers to Using Analytics Analytics: A Cross-Functional Solution to Information Overload
  • 50. Comments on Barriers Are Diverse Survey Comments on Barriers to Growth in Use of Analytics “Resistance comes from most experienced, those requiring 100% accuracy” “Access to critical data 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 drives decisions”
  • 51. With Some Skepticism Still There “The importance placed on analytics will grow, however there will be a disproportionate reliance placed on results, until management learns that garbage in/garbage out continues to cast its shadow.“ “It really doesn’t matter as most data currently produced comprises the basis for most uses necessary. Advanced techniques do not therefore produce ‘advanced’ data - the numbers are the numbers no matter how produced. Indeed, give me a room full of ladies in green eyeshades and Marchant calculators and maybe a punch card reader or two and I could be perfectly happy with managing the business, no matter how complex.“ “Those companies that do not embrace technology and analytics will be left behind in the dust of those companies that do. “ Analytics: A Cross-Functional Solution to Information Overload
  • 52. 3 Guidelines to Implementing Analytics 1. Have executive sponsored roadmap clearly outlining: § 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, proficient with analytics. § Every person does not have to meet both qualification. § A team can be used with some business and some analytics experts.
  • 53. And Keep Your Eyes On Legal Landscape § Stored Communications Act • Fourth Amendment • Enacted on 10/21/1986 • Requires insurers to tell policyholders if an action detrimental to them is taken as a result of the collection of electronic data. § Case Law Precedent • Roman v. Steelcase • Copes v. State Farm • Largent v. Reed
  • 54. Retrospective versus Predictive
  • 55. Questions and Discussion Thank You For Your Time! Enjoy the Conference Steven Callahan, CMC®, FFSI www.linkedin.com/in/stevencallahan Steve_Callahan@renolan.com The Nolan Company www.renolan.com