Data Driven Insurance Underwriting

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A discussion on how insurance companies could use telematics data, social media and open data sources to analyse and better price policies for their customers

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Data Driven Insurance Underwriting

  1. 1. Data Driven Insurance Underwriting David M Walker Data Management & Warehousing http://datamgmt.com 26 November 2013
  2. 2. What happens when you add a little black box to a car? •  This small box can be fitted to a car in about an hour •  A basic model collects the following info: –  Longitude, Latitude & Altitude –  X, Y & Z Acceleration –  Speed –  Direction of travel –  Distance travelled since last report –  Box Identifier –  Date & Time 26 November 2013 http://datamgmt.com 2
  3. 3. On device collection of data in a Round Robin Database •  A Round Robin Database or Circular Buffer records the data regularly (e.g. milliseconds) •  After a time interval or a distance travelled an aggregate report is sent to the server (Usage Reports) –  Usage reports can be separately buffered if there is no data transmission signal •  If a crash occurs the entire content of the buffer is sent to the server (Crash Reports) 26 November 2013 http://datamgmt.com 3
  4. 4. Sending Data Home 1.  Black  Box  sends  data  to  central  colla1ng   server(s)  over  mobile  data  networks   2.  Colla1ng  server(s)  send  data     files  to  MMP  Analy1cal  Server   Geo   Info   Under-­‐ wri1ng   Claims   ERP     CRM 26 November 2013 3.  Supplement  the  data  with  informa1on   from  the  opera1onal  systems  and   external  data  sources   http://datamgmt.com 4
  5. 5. Usage Reports •  In town driving typically generates more time limited reports –  Short distances driven in slow traffic –  Typically every 15 seconds •  Motorway driving typically generates more distance limited reports –  Long Distances driven at high speed –  Typically every 1 km •  Trips are defined as ignition start to ignition stop •  Usage reports describe the driving behaviour 26 November 2013 http://datamgmt.com 5
  6. 6. Crash Reports •  How was the driver driving immediately prior to the crash? •  Where did the impact come from? –  X, Y & Z Acceleration determine point of impact •  negative acceleration on the front – you hit them •  positive acceleration on the back – they hit you •  Crash reports are used to help determine fault 26 November 2013 http://datamgmt.com 6
  7. 7. Basic Data Model Social  Media   Policy  Holders   Drivers   Policies   Policy  Holder   Underwri1ng   Data   Driver   Underwri1ng   Data   Vehicles   Geographic     Layer   Informa1on   Claims   Data   Points   Crashes   26 November 2013 Trips   http://datamgmt.com 7
  8. 8. Data Volumes •  88 – 290 Bytes Per Data Point –  Depends on the type of Black Box •  124 Data Points per Trip on average •  81 Trips per Month per Vehicle •  Retained over 5 years –  ~ 165 Mb per customer –  ~ 1 Tb per 7000 customers –  ~ 15 Tb for 100,000 customers •  All the rest is insignificant –  Policies, claims, reference data, etc. becomes insignificant in comparison to trip data 26 November 2013 http://datamgmt.com 8
  9. 9. Data Collected At Quote Time •  Vehicle –  Make, Model, Engine Size, Alarm, Modifications, #Seats, Where kept (Day & Night), Use (Social, Domestic, Commuting, etc.), Annual Mileage •  Policy Holder & Other Drivers –  Address, Age, Gender§, Marital Status, #Children, Other Vehicles, Employment Status, Occupation, Industry, Residency, Previous Claims & Convictions, Licence Type & Additional Qualifications, Medical Conditions §Gender  prohibited  as  ra1ng  factor  by  European  Court  of  Jus1ce  aTer  21st  December  2012   26 November 2013 http://datamgmt.com 9
  10. 10. Data Collected At Claim Time •  •  •  •  •  •  •  •  •  •  •  Type of Incident Location of Incident Weather Other Parties Involved Types of Vehicles Involved Injuries Damage to vehicles Damage to third parties and property Police Involvement Description of the incident Photographs and Sketches 26 November 2013 http://datamgmt.com 10
  11. 11. Data Collected about Geography •  Commercial & Government Sources –  Road Name, Road Type, Speed Restrictions –  Average Speed by Road, by Day, by Day of Week and by Time of Day –  Points of Interest •  Supermarkets, Petrol Stations, Car Parks, Theme Parks, Sports Stadiums, etc. –  Meteorological Information •  Rainfall, Temperature, Sunrise/Sunset Times •  Open Sources –  Wikipedia –  Google/Bing/Apple Maps 26 November 2013 http://datamgmt.com 11
  12. 12. Data Collected from Social Media •  Customer ‘Likes’ the insurance company on Facebook –  “Wow - just got a great deal on my car insurance” •  Customer chats to their friends –  “Just had a bump in my car, going to try and get them for whiplash too!” •  Yes – people really are that stupid! 26 November 2013 http://datamgmt.com 12
  13. 13. Advanced Data Collection •  More sensors in the little black box •  Vehicle Interface Modules (VIMs) –  Provide an interface between a vehicle's on-board diagnostics link (e.g. OBD II) and the black box –  Depending on the vehicle this provides access to data such as oil/water/tyre pressures, time since last service, car dashboard warning lights that are on, ABS usage, airbag deployment, was the Bluetooth active, lights on/off, windscreen wipers on/off etc. 26 November 2013 http://datamgmt.com 13
  14. 14. A Note On Data Privacy •  Data Protection & Privacy Laws –  These vary by country so just how much you can use of what you could collect will also vary •  You can’t use all the data anyway –  European Court of Justice banned insurance companies from using gender as a rating factor after 21st December 2012 •  Opt-in/Opt-out data usages –  It is also possible, with permission, to resell individual and aggregate information to third parties 26 November 2013 http://datamgmt.com 14
  15. 15. Creating an Insurance Offering •  Offerings are typically designed a lot like ‘Pay As You Go’ Mobile contracts –  Fixed element – covers the device cost, administrative aspects, etc. –  Usage (Risk) element – price per km driven, with different rates for different levels of service •  e.g. driving in the rush hour or the dark carries a higher price than driving off-peak in daylight –  Usage bundles – First 500 km included per month •  Requires top-up once they are all used up otherwise you are not insured to drive – normally auto debited from a credit card 26 November 2013 http://datamgmt.com 15
  16. 16. Unexpected Consequences •  Driver behaviour is modified but this may not deliver the expected results hVp://www.dailymail.co.uk/news/ar1cle-­‐2359150/Teenage-­‐driver-­‐passenger-­‐died-­‐broke-­‐limit-­‐beat-­‐11pm-­‐insurance-­‐curfew.html   26 November 2013 http://datamgmt.com 16
  17. 17. New Business & Renewal Quotations •  Year 1 Underwriting –  New policy prices based on traditional (nontelematics) underwriting scores –  No renewals •  Year 2+ Underwriting –  New policy prices based on data about existing customers and vehicles with similar profiles –  Renewals based on the individual risk profile 26 November 2013 http://datamgmt.com 17
  18. 18. (Dis)-Incentivising •  Carrots –  Bonus miles for driving within speed limits, in daylight, off-peak, good weather, parking offroad, etc. •  Sticks –  Increased cost per km for persistent speeding, regular hard braking (detected from accelerometer), etc. –  Note: Penalising customers too hard will force them to move away and have a reputational impact 26 November 2013 http://datamgmt.com 18
  19. 19. So what does the data show? •  •  •  •  •  •  •  Driving Behaviours Policy Compliance Claim Assessment First Responder Theft & Fraud Risk Profiling Customer Behaviours 26 November 2013 http://datamgmt.com 19
  20. 20. Driving Behaviours •  Does a person driving follow speed limits? –  Average speed as a percentage of the speed limit by roadtype, user and between dates •  Does the person regularly brake hard? –  # of negative X-Accerations by 1000 miles driven by roadtype, user and between dates •  Does the person drive unduly long hours? –  Number of trips longer than X hours –  Number of minutes break between trips 26 November 2013 http://datamgmt.com 20
  21. 21. Policy Compliance •  Total number of miles driven •  Is a vehicle registered for Social, Domestic & Pleasure being used for commuting or business –  Regularly driving between A and B in the morning and between B and A in the evening •  Location where the car is parked over night –  Usually at a point near the policy holders address or somewhere completely different •  Taxi & Delivery Drivers –  Don’t buy a commercial policy but can be spotted by their driving patters 26 November 2013 http://datamgmt.com 21
  22. 22. Claim Assessment •  When a claim is made the details can be verified –  Location of accident – even have a look at it on Google Maps –  Point of collision and who hit whom –  Weather, Amount of Light –  Speed and G Forces at time of impact –  Did the vehicle roll? 26 November 2013 http://datamgmt.com 22
  23. 23. First Responder •  When an accident occurs: –  If it is severe enough try and contact the customer –  Contact emergency services if required –  Arrange for your preferred recovery/repairers to deal with the incident reducing the claim costs –  Perception bonus – My insurance company really cares for me! 26 November 2013 http://datamgmt.com 23
  24. 24. Theft & Fraud •  Theft –  Device is always tracking so if a vehicle is reported stolen it can traced and recovery action •  Fraud –  Fraud rings may fake traffic accidents or stage collisions to make false insurance or exaggerated claims –  Many of the details can now be validated (location, weather, speed, collision, etc.) 26 November 2013 http://datamgmt.com 24
  25. 25. Risk Profiling •  What combination of attributes for both a driver and a vehicle have the lowest total claim value per 100,000 miles driven? •  Are a larger number of small claims more expensive than a smaller number of large claims? •  Statistical Cluster Analysis techniques to determine high and low risk proposals 26 November 2013 http://datamgmt.com 25
  26. 26. Customer Behaviour •  Football Supporter –  Regularly goes to home ground –  Do they go to away matches too? •  Business Traveller –  Regularly leaves car at airport parking •  School Run –  To and from home to local school twice a day •  Change of job –  Changes location of daily commute parking •  This information can (with permission) be sold to third parties –  Marketing companies, Football clubs, etc. –  These techniques are already being used by some mobile companies 26 November 2013 http://datamgmt.com 26
  27. 27. Security Services •  Fact Of Life •  Courts will order access to data if someone is under suspicion –  Anti-Terrorism, Organised Crime, etc. •  Data will be used after an event to track –  Where did they travel from –  Who did they visit before the act –  etc. 26 November 2013 http://datamgmt.com 27
  28. 28. The Future •  Pay As You Go Road Usage Pricing –  Governments requiring cars to be fitted with telematics and road usage data sent to them •  Reduced Premiums & Higher Profits –  If all cars have telematics then low risk customers will not be used to subsidise high risk customers – some of this benefit is passed on to the consumer by way of lower premium and some is retained by the insurance company 26 November 2013 http://datamgmt.com 28
  29. 29. An Observation •  Some of the evidence from telematics is either counter-intuitive or goes against what the underwriters ‘know’ is right •  Getting business users to use the data and adjust the way they rate risk is difficult •  If you make changes to how risk is rated you have to track the effect of the changes 26 November 2013 http://datamgmt.com 29
  30. 30. Who’s doing this in the UK ? 26 November 2013 http://datamgmt.com 30
  31. 31. Have a play … •  InstaMapper GPS Tracker –  http://www.insta-mapper.com –  iPhone & Android App –  Gives GPS but not accelerometer data •  Other applications are available but this is the one I used for the Proof of Concept work 26 November 2013 http://datamgmt.com 31
  32. 32. David M Walker Data Management & Warehousing THANK YOU 26 November 2013 http://datamgmt.com 32
  33. 33. Contact Us •  Data Management & Warehousing –  Website: http://www.datamgmt.com –  Telephone: +44 (0) 118 321 5930 •  David Walker –  E-Mail: davidw@datamgmt.com –  Telephone: +44 (0) 7990 594 372 –  Skype: datamgmt –  White Papers: http://scribd.com/davidmwalker 26 November 2013 http://datamgmt.com 33
  34. 34. About Us Data Management & Warehousing is a UK based consultancy that has been delivering successful business intelligence and data warehousing solutions since 1995. Our consultants have worked with major corporations around the world including the US, Europe, Africa and the Middle East. We have worked in many industry sectors such as telcos, manufacturing, retail, financial and transport. We provide governance and project management as well as expertise in the leading technologies. In The Netherlands Data Management & Warehousing works in partnership with DeltIQ Group. 26 November 2013 http://datamgmt.com 34
  35. 35. Data Driven Insurance Underwriting David M Walker Data Management & Warehousing http://datamgmt.com THANK YOU

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