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Innovation and Big Data in Insurance

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Innovation and Big Data in Insurance

  1. 1. Big Data an actuarial perspective Decavi-KPMG,18/11/2015 MateuszMaj Chairmanof IABE BigDataWG mat@motosmarty.com
  2. 2. What is Big Data?
  3. 3. Internet of Things
  4. 4. What is Big Data?
  5. 5. What is Big Data?
  6. 6. What is Big Data? source:www.hp.com
  7. 7. Why Big Data Working Group? Discuss: •  Impact of Big Data on insurance sector and the actuarial profession; •  Present challenges and good practices when working with Big Data; •  Educate actuarial profession about Big Data through CPD courses
  8. 8. Insurance value chain: undewriting Covers different Underwriting 360degree customerview  
  9. 9. Combine different sources and apply analytics to create comprehensive customer view and: •  Maximize profitability of the current portfolio •  Detect cross-sell and up-sell opportunities; •  Increase customer satisfaction and loyalty; •  Acquire new profitable customers and reduce marketing costs. Underwriting
  10. 10. Underwriting Tescogroup–UK Motor – 1M Pet – 0.45M Travel - 0.175M Life – 0.175M Home – 0.4M  
  11. 11. •  Insurance prevention program with discounts and rewards for good driving •  ‘Phased’ approach: •  Phase 1: combine data from different sources i.e. traditional channels, online channels, external service providers, Tesco group warehouses; •  Phase 2: Identify the right customers within Tesco network; •  Phase 3: Provide initial offer and reward drivers with initial rewards from Tesco group; •  Phase 4: Iterate and provide personalized insurance offers. Underwriting makinginsurancesexy
  12. 12. Pricing
  13. 13. Pricing Ratingtrends 1980s Now Profession Engine power Coverage Bonus-malus Coverage Bonus-malus Claims history Traffic violation history Age of vehicle Use of vehicle Make of vehicle Purchase price Parking place Occupation No. of drivers Age of drivers Maritial status Real estate Driving license Mileage Registered owner Credit rating … Do we need additional factors? Is telematics necessary?
  14. 14. Univariate basis Risk modelling Technical premium modelling Scenario testing Price optimisation Extra data sources Telematics data? Pricing Ratingtrends
  15. 15. •  New rating factors; •  Flexible, dynamic risk pricing; •  New modelling techniques like machine learning; •  New, disruptive insurance offerings like Usage- Based Insurance. Pricing
  16. 16. Pricing Usage-BasedInsurance(UBI) UBI is the scheme where insurance premiums are calculated based on dynamic causal data, including actual usage and riskier driving behavior.
  17. 17. Pricing Usage-BasedInsurance(UBI) Full  UBI  –  niche  market  segments     Motor  insurance  telema7cs  –  mass  market   Works  well  with  niche  segments:   •  Young  drivers   •  People  living  in  risky  regions   •  Low-­‐mileage  drivers   Insure  the  Box,  UK   New  disrup=ve  model  for  low-­‐mileage  drivers  with  prepaid   miles  (similar  to  prepaid  mobile.  No  punishment  for  bad   driving.    Self-­‐selec=on  +  individualized  premiums.   Progressive,  US   “Prince  of  pricing”  with  MyRate  (pricing  variable  innova=on)   &  Snapshot  (1st  telema=cs-­‐based  insurance)  offers   Self  selec=on  &  individualized  insurance  premiums   Intesa  SanPaulo  Assicura,  IT   Telema=cs  play  key  posi=on  –  car,  bike  home   Created  viable  risk-­‐based  pricing  model  with  phased   approach  for  Viaggia  con  me:     Phase  1  (tradi=onal  variables),  Phase  2  (Banking  parameters),   Phase  3  (Purchase  and  use  variables),  Phase  4  (Behaviour   variables)  
  18. 18. Insurance value chain: undewriting Covers different Claims management & Fraud detection Insurers loose 5% of the annual revenue due to fraud Coalition Against Insurance Fraud (US) in the 2014 report has stresses that technology & Big Data plays a growing role in fighting fraud
  19. 19. Claims management Examples-UBI From high to low loss ratios UnipolSai  -­‐  IT CoverBox  &  Carrot  -­‐  UK Telema=cs  champion  (2.2M  ac=ve  boxes)   Best  prac=ce  claims  management  incl.:   •  FNOL  -­‐  quick  accident  response   •  Vehicle  loca=on  in  case  of  of  theW   •  Accident  reconstruc=on Further improvement of the operational efficiency including: •  Crash data combined with video footage to fight fraud •  Better prediction methods to reduce claims duration and cost i.e. no need for expert, efficient accident reconstruction •  Prove innocence
  20. 20. Covers different Legislation EU-widelaw underconstruction  
  21. 21. Insurance Canitbesexy? Health insurance, US Oscar is a health insurance company that employs technology, design, and data to humanize health care. Technology as differentiator (telemedicine) - system connects doctors and patients with help of mobile app, website, bills, free fitness-tracking devices – easy to use and understand.
  22. 22. Insurance Canitbesexy? Home Insurance, IT Habit@t is the first Connected Home Insurance by Cardiff; Well targeted customers – young, tech-savvy, from big towns; IoT based - combining home telematics with home insurance; Superior customer relationship - user-friendly & seamless usage via smartphone app, multichannel with strong support; Proactive alerts and risk managements.
  23. 23. EU-widelaw underconstruction   Role of actuaries Datascientists  
  24. 24. MateuszMaj Chairmanof IABE BigDataWG mat@motosmarty.com Q&A  

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