Leveraging Big Data at CED
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Leveraging Big Data at CED - INSEAD alumni event hosted by the Boston Consultancy Group
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Leveraging Big Data at CED
- 1. Big Data Analytics event Leveraging Big Data Amsterdam, May 14, 2014
- 3. 3 • Introduction to CED • History • Key figures BUSINESS BACKGROUND • Maturity model • Straight Through Processing • Data driven propositions BIG DATA • Market trends • Strategic choices • Challenges STRATEGY • Please feel free to ask any further details QUESTIONS Agenda 1 2 43
- 5. 5 “The whole claims process at one company”
- 6. 6 • Founded in 1971 • Number of employees 1.150 • Offices in 14 European countries • Partners in 28 countries worldwide • Handles approx. 1 million claims per year • With a total claim cost of more than 2bn euro • Revenue FY2013: 105 million euro Key figures
- 7. 7 Core businesses LOSS ADJUSTMENT DIRECT REPAIR MANAGEMENT CLAIMS MANAGEMENT
- 8. Strategy
- 9. 9 • Due to safer infrastructures and safer cars. • Consumers demand high service standards and web/mobile channels. • Due to Solvency II and the will to improve Combined Ratios. • Other forms of damage assessment e.g. based on statistics and big data. Substitutes Cost savings Less damages Role of the consumer “Market trends demand a shift in strategic focus”
- 10. 10 Loss adjustment Claims Management Direct Repair “Market trends demand a shift in strategic focus” Loss adjustment Claims Management DirectRepair Visual representation current revenue distribution Visual representation future revenue distribution
- 11. 11 • The integration of Claims Management with Loss adjustment and Direct Repair • First Contact Resolution Full service provider • Decision making at FNOL • Up to 80% automation (simple claims) • Manage the exceptions (complex claims) Automated Claims Handling (STP) Strategic choices
- 12. 12 Changing something we (successfully) did for the past 40 years Cannibalizing part of our core business by developing another Fighting a market image, based on our strong history Dealing with unilateral employee competencies Batteling our IT legacy Strategic challenges “Disrupt yourself before someone else will”
- 13. 13 Strategic challenges “We are not alone…”
- 14. Big Data
- 15. 15 Big Data Maturity mobel Current business model now dependent on Big Data First project(s) show clear ROI How does this work?We’re OK thanks Where do we start? Continuing to learn and enhance Creating a new business model 0 1 2 3 4 5 Level of Big Data Maturity Increasing Big Data Maturity In the dark Catching up First pilot(s) Tactical value Strategic value Optimize & extend Source: Radcliffe Advisory
- 16. 16 • Receipt and registration of the Claim • Verification of the Claim coverage and liability • Evaluation and assessment of the Claim • Financial settlement and/or Repair in kind • Client data • Premiums paid Main Claims process Notification Verification Assessment Settlement and/or Repair Coverage check (1) • Business rules • Claim history • External sources - Credit bureaus - Kadaster - Social media Fraud check Straight Through Processing
- 17. 17 • Receipt and registration of the Claim • Verification of the Claim coverage and liability • Evaluation and assessment of the Claim • Financial settlement and/or Repair in kind • Event and policy Main Claims process Notification Verification Assessment Settlement and/or Repair Coverage check (2) • Decision service for multiple party car accidents Liability check Straight Through Processing
- 18. 18 • Receipt and registration of the Claim • Verification of the Claim coverage and liability • Evaluation and assessment of the Claim • Financial settlement and/or Repair in kind • Based on statistical and technical filters estimation of the damage Main Claims process Notification Verification Assessment Settlement and/or Repair Evaluation of claim • Based on business rules initiate payment or repair service Settlement service • Based on business rules decide the repair type and available and specialized repairer Repair service Straight Through Processing
- 19. 19 Data driven propositions Core business process Data generation Data driven propositions
- 20. 20 Data driven propositions Damage history check for cars •(eg. website or app) Claim sensitive locations •(eg. Insurers or local government) Calculate your damage •(eg. Consumers: car or property) Claims data for improving creditscore •(eg. Banks or insurers) Perform fraud analytics and/or define fraud rings •(eg. Insurers or lease companies)
- 21. 21 “From gameshow rockstar to worlds best claims handler?”
- 22. 22 “From gameshow rockstar to worlds best claims handler?”
- 23. “From gameshow rockstar to worlds best claims handler?”
- 24. 24
- 34. 34 Conclusion Recognize the strategic importance Organize innovation the right way Start with business models in mind Combine internal and external sources 1 2 3 4
- 35. Rietbaan 40 – 42 Postbus 393 2900 AJ Capelle aan den IJssel The Netherlands T +31 (0)10 284 34 34 info@ced-claimexperts.nl Thank you for your attention Jochem Davids