Insurance telematics uses big data from vehicle tracking to provide more accurate driver profiles and pricing, allowing insurers to better segment risks. However, analyzing and utilizing large amounts of driving data presents new challenges for insurers and requires capabilities beyond their traditional core competencies. While telematics offers potential for growth and competitive advantage, insurers must overcome headaches around collecting, storing, and deriving insights from massive driving data sets.

1. Insurance Telematics:
Big Data, Big Potential, Big Headache
Dave Huber, President
Kairos Solutions
IFSUG March 2012

2. Big Data
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3. One of the few products whose price is set
before costs are known
Known costs Unknown costs
O Loss adjustment expense O Pure premium (freq x sev)
O Operations O Bodily injury
O Advertising O Comp & Collision
O Underwriting O Regulatory
O Commissions O Trends
Known costs
Unknown costs
Premium
Data drives insurance decisions 3

4. Pricing sophistication is a competitive
advantage and depends on data analytics
O Granularity
O The number of pricing cells per question or variable
O Age: 16-19, 20-25, 26-30…vs. 16, 17, 18, 19….
O Dispersion
O The range of rates for each of the variables
O $450-$900 vs. $225-$1375
O Interactions
O The lift when combining variables
O Vehicle symbol & territory – pickups in suburbs
O Variables
O New questions and/or external data
O Credit, occupation, prior limits
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5. Insurers generally use the same data to price
$1000 $1000
31 Age 31
M Gender M
S Marital status S
Speed Violations Speed
4 Points 4
Own Homeowner Own
Y Prior insurance Y
611 Credit 611
YMM Vehicle YMM
These drivers look like Pure Premium Carbon Copies and are priced identically
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6. But imagine knowing something about drivers
that no one else knows
$800 $1200
31 Age 31
M Gender M
S Marital status S
Speed Violations Speed
4 Points 4
Own Homeowner Own
Y Prior insurance Y
611 Credit 611
YMM Vehicle YMM
10,651 Verified Annual Miles 13,182
4.9 Trips per day 6.1
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So they’re NOT Pure Premium Carbon Copies after all…and they deserve a different price

7. Usage-Based Insurance is all about
segmentation & pricing
O How, when & where you drive
O Driving data’s not readily available &
expensive to collect
O Need a lot of driving data
O Beyond insurers’ core competency
O Insurers would really like a driving score
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8. The pricing advantage of UBI data is big
O Granularity
O The number of pricing cells per question or variable
O Age: 16-19, 20-25, 26-30…vs. 16, 17, 18, 19….
O Self-reported mileage buckets vs. verified continuous mileage
O Variables
O New questions and/or external data
O Credit, occupation, prior limits
O How, when & where, self-selection, personal driving score akin to a
credit score
O Interactions
O The lift when combining variables
O Vehicle symbol & territory – pickups in suburbs
O Miles x time of day, frequency & magnitude of speed changes, speed x
traffic
O Dispersion
O The range of rates for each of the variables
O $450-$900 vs. $225-$1375
O Personalized pricing
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9. So what does when, where & how look like?
O Time-stamped trip start/stop, engine on/off
O OBD - vehicle speed every second
O GPS - lat, long & heading every second
O Accelerometer – 3 axis acceleration
How big is Big Data?
O 5,000 GPS-enabled devices
O 8MM journeys & 15B journey points
O 20 million new rows of data daily
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10. How might all this Big Data show up?
 Annual mileage  Miles in territory  Driver score
 Avg trip duration  Drive time in territory  Driver “footprint”
 Avg trip length  Idle time in territory  Left turns
 Trips per day  Cornering  Speed variation
 Trips per time of day  Lateral acceleration  Trip type (speed vs time)
 Journeys  Rolling stops  Territory by time of day
 Miles by time of day  Self-selection  Holiday driving
 Miles by day of week  Lane changes  School zone
 Weekdays  Acceleration events in  Violations by trip type
 Weekends speed bands  Trip radius
 Miles in speed bands  Braking events in speed  Student profile
 Time in speed bands bands  Intersections
 Average speed  Frequency of speed  Turn signal
changes
 Trip regularity (miles)  Seat belt
 Magnitude of speed
 Trip regularity (time) changes  Lights / wipers
 Aggressive acceleration  Commuter profile  Vehicle maintenance
per 100 miles  Time between
 Errand-runner profile
 Aggressive braking per trips/journeys
100 miles  Coffee drinkers
 Congestion index
 Road type  YMM relativities
 Summer car
 Relative speed  OnStar subscription
 Texting & phone use 10
 Cruise control

11. Big Potential
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12. Growth depends on acquisition & retention
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13. Driving data colors the opportunity
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14. But insurers without UBI are color blind
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15. UBI book attracts preferred drivers who
are accurately priced…
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16. Insurers without UBI are left with a book
that looks like this to them…
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17. But in reality behaves like this…
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