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  • Insurance Telematics: Big Data, Big Potential, Big Headache

    Dave Huber, President

    Kairos Solutions

    IFSUG March 2012

  • Big Data

    2

  • One of the few products whose price is set before costs are known

    Known costs Unknown costs

    3

    O Loss adjustment expense

    O Operations

    O Advertising

    O Underwriting

    O Commissions

    O Pure premium (freq x sev)

    O Bodily injury

    O Comp & Collision

    O Regulatory

    O Trends

    Known costs

    Unknown costs Premium

    Data drives insurance decisions

  • 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, 26-30vs. 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

    4

  • Insurers generally use the same data to price

    5

    Age

    Gender

    Marital status

    Violations

    Points

    Homeowner

    Prior insurance

    Credit

    Vehicle

    31

    M

    S

    Speed

    4

    Own

    Y

    611

    YMM

    31

    M

    S

    Speed

    4

    Own

    Y

    611

    YMM

    These drivers look like Pure Premium Carbon Copies and are priced identically

    $1000 $1000

  • But imagine knowing something about drivers that no one else knows

    6

    31

    M

    S

    Speed

    4

    Own

    Y

    611

    YMM

    10,651

    4.9

    31

    M

    S

    Speed

    4

    Own

    Y

    611

    YMM

    13,182

    6.1

    $800 $1200

    Age

    Gender

    Marital status

    Violations

    Points

    Homeowner

    Prior insurance

    Credit

    Vehicle

    Verified Annual Miles

    Trips per day

    So theyre NOT Pure Premium Carbon Copies after alland they deserve a different price

  • Usage-Based Insurance is all about segmentation & pricing

    O How, when & where you drive

    O Driving datas 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

    7

  • 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, 26-30vs. 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

  • How big is Big Data?

    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

    9

    O 5,000 GPS-enabled devices

    O 8MM journeys & 15B journey points

    O 20 million new rows of data daily

    So what does when, where & how look like?

  • How might all this Big Data show up?

    10

    Annual mileage

    Avg trip duration

    Avg trip length

    Trips per day

    Trips per time of day

    Journeys

    Miles by time of day

    Miles by day of week

    Weekdays

    Weekends

    Miles in speed bands

    Time in speed bands

    Average speed

    Trip regularity (miles)

    Trip regularity (time)

    Aggressive acceleration per 100 miles

    Aggressive braking per 100 miles

    Road type

    Relative speed

    Miles in territory

    Drive time in territory

    Idle time in territory

    Cornering

    Lateral acceleration

    Rolling stops

    Self-selection

    Lane changes

    Acceleration events in speed bands

    Braking events in speed bands

    Frequency of speed changes

    Magnitude of speed changes

    Commuter profile

    Errand-runner profile

    Coffee drinkers

    YMM relativities

    OnStar subscription

    Cruise control

    Driver score

    Driver footprint

    Left turns

    Speed variation

    Trip type (speed vs time)

    Territory by time of day

    Holiday driving

    School zone

    Violations by trip type

    Trip radius

    Student profile

    Intersections

    Turn signal

    Seat belt

    Lights / wipers

    Vehicle maintenance

    Time between trips/journeys

    Congestion index

    Summer car

    Texting & phone use

  • Big Potential

    11

  • 12

    Growth depends on acquisition & retention

    12

  • Driving data colors the opportunity

    13

  • 14

    But insurers without UBI are color blind

    14

  • UBI book attracts preferred drivers who are accurately priced

    15

  • Insurers without UBI are left with a book that looks like this to them

    16

  • But in reality behaves like this

    17

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