FastTrack Analytics for Insurance

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Learn how insurance organizations can leverage FastTrack Analytics to improve financial, underwriting, agent & customer service operations. Use your data to gain competitive advantage in challenging economic times.

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FastTrack Analytics for Insurance

  1. 1. FastTrack! Analytics for Insurance Webinar<br />May 12, 2010<br />
  2. 2. Agenda<br />
  3. 3. Edgewater’s Corporate Overview<br /><ul><li>Consulting Firm
  4. 4. Advisory Services
  5. 5. Vertical Services
  6. 6. Enterprise Applications
  7. 7. Strategic Technical & Data Services
  8. 8. Founded in 1992
  9. 9. Headquartered in Wakefield, MA
  10. 10. 335 Employees
  11. 11. Large North American Footprint
  12. 12. Focus on Middle and Global 2000 Market
  13. 13. Publicly Traded (NASDAQ: EDGW)</li></li></ul><li>Edgewater’s Insurance Practice<br />Insurance Clients<br />Industry Segment<br />Life and Annuity Insurers<br />P&C Insurance Companies<br />Medical Malpractice<br />Disability Insurers<br />Brokers and Agencies <br />Insurance Marketplace Software Products & Services<br />Trade Associations<br />
  14. 14. Why Analytics for Insurance?<br />Why should I be considering analytics?<br />Enhanced customer service, extendable to Agents<br />Move from avalanche of data to actionable information<br />Ad-hoc capabilities, remove the need for IT <br />Analyzing information vs. searching for data<br />Self-service access to key data points<br />Visual hotspots, no more searching reports<br />
  15. 15. FastTrack! Analytics – Insurance Focus Areas<br />Build on the core dimensions by adding others to address specific business questions in one of these areas:<br />Customer Service<br />(Policy status, cash value, complementing products)<br />Operations(Efficiency, Planning, Forecasting, Quality <br />Metrics)<br />Core Dimensions:<br />Policy, Product, Client, Group, Eligibility, Claim<br />Core Metrics:<br />Sales, Persistency, Status, Premiums. Losses<br />Agency(Products across region, TPA tracking, Agency production, Lapse rates, license / appointment tracking)<br />Actuarial (Product types by region, profitability, rates)<br />Finance(Planning and Budgeting Metrics, Customer or Item Profitability)<br />Underwriting(Policy Status, Time to Issue, Declination rates, Replacements)<br />
  16. 16. Insurance Analytics – Operations Focus Area<br />Potential Operations Analytics Subject Areas<br />Efficiency<br />Planning<br />Logistics<br />Forecasting<br />Quality<br />Sample KPI’s<br /><ul><li>Active Submissions by Underwriter
  17. 17. Time to decline by Underwriter
  18. 18. Rewrites by Underwriter
  19. 19. Open Claims by Adjuster</li></ul>Sample Questions<br /><ul><li>Who are my most efficient team members?
  20. 20. Where are we wasting time in our core processes?
  21. 21. Where are the quality hotspots in the organization?</li></ul>Potential Benefits<br /><ul><li>Optimize Business Processes
  22. 22. More effective training procedures
  23. 23. Minimize Compliance issues</li></li></ul><li>Insurance Analytics – Actuarial Focus Area<br />Potential Actuarial Analytics Subject Areas<br />Product Type Analysis<br />Profitability<br />Rate Analysis<br />Sales<br />Rider Take Up<br />Sample KPI’s<br /><ul><li>Sales by product by Age
  24. 24. Premiums vs. Lapses / Claim</li></ul>Sample Questions<br /><ul><li>Which products are sold in which region / age band? What combination of optional riders are taken up?
  25. 25. How profitable are my products? Claim ratio? Lapse rates?</li></ul>Potential Benefits<br /><ul><li>Ability to amend product offerings, optional vs. built in riders, tailored to the market
  26. 26. Understand key profit vs. risk</li></li></ul><li>Insurance Analytics – Underwriting Focus Area<br />Potential Underwriting Analytics Subject Areas<br />Policy Status<br />Underwriting Efficiency<br />Underwriter Performance<br />Results Analysis<br />Bottle Necks<br />Sample KPI’s<br /><ul><li>Time to Issue by Product
  27. 27. Declination Rates by region / product / age band
  28. 28. Policy Profitability by Underwriter</li></ul>Sample Questions<br /><ul><li>How long does it take to issue policies from application? Why?
  29. 29. What are my issue vs. declination / NTU rates by “factor”?</li></ul>Potential Benefits<br /><ul><li>Smoother faster Underwriting workflow?
  30. 30. Identify possible product issues?</li></li></ul><li>Insurance Analytics – Customer Service Focus Area <br />Potential Customer Service Subject Areas<br />Customer Service Performance<br />Customer Satisfaction<br />Cross-Sell/ Upsell<br />Key Product Analysis<br />Optional Rider targets<br />Sample KPI’s<br /><ul><li>Products / riders by client type
  31. 31. Wait time vs. Call time and transaction volumes / types</li></ul>Sample Questions<br /><ul><li>What are the most popular optional riders and products for the client? What cross / up sell could gain traction?
  32. 32. What are my most common CSR call requests? What transactions and how long do they take to perform?</li></ul>Potential Benefits<br /><ul><li> Increased product sales by targeted messaging
  33. 33. Identify key target transaction for online rollout or performance improvement</li></li></ul><li>Insurance Analytics – Agency Focus Area<br />Potential Agency Analytics Subject Areas<br />Agency Performance<br />Incentives<br />Agent Loyalty<br />CSR<br />Performance<br />Agency Distribution<br />Sample KPI’s<br /><ul><li>Policy Profitability by Agent
  34. 34. Premium by CSR
  35. 35. Claims over threshold by Agent</li></ul>Sample Questions<br /><ul><li>Who are my best performing producers?
  36. 36. Where does my “best” business come from?</li></ul>Potential Benefits<br /><ul><li>Better agent performance
  37. 37. Eliminate unprofitable producers</li></li></ul><li>Insurance Analytics – Finance Focus Area<br />Potential Finance Subject Areas<br />Planning<br />Budgeting<br />Customer Profitability<br />Product/ Item Profitability<br />Sample KPI’s<br /><ul><li>At risk vs. reinsurance
  38. 38. Cost to roll out a new product
  39. 39. Target high value customer segment
  40. 40. Product revenue forecast projections </li></ul>Sample Questions<br /><ul><li>What are my at risk areas?
  41. 41. What are my most profitable product lines?
  42. 42. What is the ROI on my marketing campaigns? </li></ul>Potential Benefits<br /><ul><li> Allow for growth / expansion without assuming undue risk
  43. 43. Focus efforts on more profitable products
  44. 44. Increased ROI on budget allocations</li></li></ul><li>Why Clients Haven’t Pursued Analytics<br /><ul><li>“I’ve heard about data analytics, but don’t really understand what it means or how it would benefit us.”
  45. 45. “It required too large of an investment to get started”
  46. 46. “I’m just too busy and short-staffed to get involved with a major initiative like data analytics”
  47. 47. “I’ve looked at several data analytics software packages, but it doesn’t really fit our unique business”
  48. 48. “I already get all the reports I need, why would I want more?”</li></li></ul><li>What is FastTrack! Analytics?<br />The FastTrack! Analytics is a customizable, cost sensitive, business-issue focused solution-based approach designed to quickly demonstrate the power of analytics to your business<br /><ul><li>Not a “shrink-wrapped” product that doesn’t fit your business model
  49. 49. Based on accelerators that can be quickly customized to meet your specific business needs
  50. 50. Allows for expansion at your pace</li></ul>Customizable<br /><ul><li>Designed to provide insights into the your most challenging business questions
  51. 51. Not a generic product that doesn’t always provide the answers you need most</li></ul>Business Issue Focused<br /><ul><li>Doesn’t require a high up-front investment (in both dollars & resources)
  52. 52. Designed to leverage current in-house technology
  53. 53. Allows for expansion at your pace</li></ul>Cost/Risk Sensitive<br />
  54. 54. What are the Benefits of FastTrack! Analytics?<br />
  55. 55. How does FastTrack! Analytics Work?<br /><ul><li>Confirm the subject area and business problem(s) in scope
  56. 56. Define/confirm the data visualization capabilities required
  57. 57. Decompose the business problem(s) into required data
  58. 58. Confirm the high-level business-centric data model
  59. 59. Determine which accelerator components to use
  60. 60. Assemble the analytics data mart data model
  61. 61. Design/develop the data visualization/ dashboard
  62. 62. Create required roles
  63. 63. Create load utilities
  64. 64. Conduct testing
  65. 65. Prep for demos
  66. 66. Conduct initial demonstration of FastTrack! Solution with key stakeholders
  67. 67. Conduct up to 2 additional demo’s
  68. 68. Document recommendations for enhancements
  69. 69. Review results of demo(s) with FastTrack! Sponsor</li></li></ul><li>How do Accelerators Work?<br />Accelerators are a collection of analytics components tailored to specific industries and are designed to significantly reduce the delivery time of analytic solutions<br />Data Visualization Component Library<br />Default Analytic Views <br />Data Model Component Library<br /><ul><li>Dashboard formats,designs & templates
  70. 70. Data visualization components
  71. 71. Analysis specific components
  72. 72. Industry specific
  73. 73. Logical groupings of components for business challenges
  74. 74. Dimensions
  75. 75. Attribute lists
  76. 76. Default hierarchies
  77. 77. Subject areas
  78. 78. Metrics groupings & KPI’s</li></ul>Extract Specification Formats<br />Key Reference Data Listings<br /><ul><li>Dimension extract formats
  79. 79. Metrics grouping extract formats
  80. 80. Look-up values
  81. 81. Code lists</li></li></ul><li>FastTrack! Analytics – Solutions Architecture<br />
  82. 82. Extending FastTrack! Analytics<br />The FastTrack! Analytics pilot can be extended in many different ways towards the creation of an enterprise analytics platform<br />More <br />Sophisticated<br />Analytics<br />Extended to Other Departments<br />Additional<br />Integrated<br />Subject Areas<br />Additional<br />Users/Roles<br />More <br />Analytical<br />Views<br />Expanded<br />Metrics<br />Additional<br />Data Sets<br />Expanded<br />Dimensions<br />
  83. 83. Q & A<br />Questions?<br />Thank You!<br />

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