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Casualty Actuarial Society
Special Interest Seminar on Predictive Modeling
CLEAR
The Canadian Loss Experience Automobile Rating
Arthur Tabachneck, Ph.D., Manager
Abdul Sattar Al-Khalidi, Ph.D., Senior Statistician
Statistical Research and Development
October 5, 2006
Casualty Actuarial Society
Special Interest Seminar on Predictive Modeling
Canada
Total Area: 3.9 mil sq miles
Population: 33 mil
Total Area: 3.7 mil sq miles
Population: 298 mil
October 5, 2006
Casualty Actuarial Society
Special Interest Seminar on Predictive Modeling
Insurance Bureau of Canada (IBC)
• Created in 1964 over concern that poor underwriting practices,
caused by inadequate information, were threatening the ability
of insurers to meet claims liabilities
• Trade organization which serves as the voice of over 90% of
the non-government P&C insurance sold in Canada
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Lobby governments for changes to public policy
Formulate industry positions
Offer organized insurance crime investigative services
Collect, validate and store information
Provide a forum for member companies to achieve the P&C
insurance industry’s common vision
October 5, 2006
Casualty Actuarial Society
Special Interest Seminar on Predictive Modeling
Government vs Non-Government Auto Insurance in Canada
Non-Government Insurers
Government Insurers
October 5, 2006
Casualty Actuarial Society
Special Interest Seminar on Predictive Modeling
Total Private Passenger Vehicles: 18,123,885
0.021 mil vehicles
0.003 mil
vehicles
0.024 mil vehicles
4.2 mil
vehicles
0.25 mil vehicles
0.076 mil vehicles
0.53 mil vehicles
2.2 mil vehicles
2.2 mil vehicles
0.66 mil vehicles
0.62 mil vehicles
6.8 mil
vehicles
0.45 mil vehicles
October 5, 2006
Casualty Actuarial Society
Special Interest Seminar on Predictive Modeling
IBC’s Department of Statistical Research and Development
• Manage databases of the entire country’s auto premium and
claims information
• Build and maintain databases of VIN, VIN groupings and
vehicle characteristics
• Assist IBC’s investigative efforts’ with all needed analytical
services
• Conduct analytical research to help identify any emerging
trends
• Develop and apply statistical models to normalize data and
estimate anticipated normalized claim data
• Provide advisory make/model/model year-specific Collision,
Comprehensive, Property Damage and Accident Benefit ratings
for all Canadian government and non-government insurers
October 5, 2006
Casualty Actuarial Society
Special Interest Seminar on Predictive Modeling
Canadian Loss Experience Automobile Rating (CLEAR)
• What
• Why
• How
October 5, 2006
Casualty Actuarial Society
Special Interest Seminar on Predictive Modeling
CLEAR
What it is
automobile insurance rating
developed in 1989 by a working group of actuaries, CIPs, IT
professionals, statisticians and underwriters
equitable and defensible
based on each make/model’s pure vehicle experience
risk ≈ rate groups (symbols)
symbols are convertible to differentials (rating factors)
Lower risks=Lower rates
October 5, 2006
Casualty Actuarial Society
Special Interest Seminar on Predictive Modeling
CLEAR
Why it was developed
most claims result in repair not replacement
price is only one of many possible predictive factors
cost saving features shouldn’t increase insurance premiums
crash test results may not reflect overall experience
a vehicle’s experience may change over time
because there are over 200 insurers, and only 18 million vehicles,
Canadian insurers have to share their data to enable credible predictions
October 5, 2006
Casualty Actuarial Society
Special Interest Seminar on Predictive Modeling
CLEAR
Why it was developed
Low relationship between price and theft claim cost
Subaru Forester 2.5 X Wagon AWD
MSRP $30,543
Honda CR-V 4 Dr AWD
MSRP $31,529
Relative Loss Cost = 388
Relative Loss Cost = 126
October 5, 2006
Casualty Actuarial Society
Special Interest Seminar on Predictive Modeling
CLEAR
Why it was developed
Low relationship between price and collision claim cost
Subaru Impreza WRX 4 Dr AWD
MSRP $39,335
Relative Loss Cost = 295
Volkswagen Passat GLS V6 4 Dr
MSRP $36,113
Relative Loss Cost = 51
October 5, 2006
Casualty Actuarial Society
Special Interest Seminar on Predictive Modeling
CLEAR - How it works
•Body style
•Drivetrain
•Wheelbase
•Weight
•Engine displacement
•Engine horsepower
•MSRP
•Indexed MSRP
•Type of brakes
•Theft deterrent system
•Track width
•Height
•Types of airbags
•Manufacturer
•Seating capacity
•Brake assistance
•Ground clearance
•Traction control
•Stability control
•Types of headrestraints
•Seatbelt pretensioners
•Lane departure warning
•Tracking system
•Parts marking
•Engine type
•Engine placement
•Age
•General model and model
October 5, 2006
Casualty Actuarial Society
Special Interest Seminar on Predictive Modeling
CLEAR - How it works
Develop and maintain
vehicle characteristics
Wheelbase: 2718
Weight: 1889
Drivetrain: 4
Price: $43,356
VCODE: 6706
Airbag: Yes
Calculate
LC & Rel LC
(AdjEstF *
AdjEstS)/
Wt Avg LC
Assure reasonability of insurance
data (for all coverages)
TDS: No
Style: SUV
ABS: Yes
Doors: 4
Year: 2007
Power: 190
Adjust estimates to
reflect actual experience
AdjESTF=ESTF(1+MAFF)
AdjESTS=ESTS(1+MAFS)
Adjust RLCs for Risk Loading and
control change with prior RLC
AdjRLC=(iprice>=65k)*
[EstRLC((iprice-45k)/i5k)*10]
Stat Plans
Error Checking
VIN Decoding
Reasonability Check
# Exposures
# Claims
Premiums
Loss
Develop/Review/Apply
Statistical M odels
Develop and apply formulae
to estimate normalized claim
frequency and severity
from vehicle characteristics
Convert AdjRLCs to Rate Groups
Rate Group=1*(AdjRLC<34.5)+
(AdjRLC/10-1.95)*(34.5<=AdjRLC<=304)
+(AdjRLC/20+13.275)*(AdjRLC>304)
Accomplish
Reversal Control
Assure reversals
are justified
Project Publication Year Fleet
Use link model based on three
most recent accident years'
exposures (as at December 31st
of each year), current year
exposures (as at June 30th), and
sales estimates
Build/Incorporate
Data Normalization Models
Est Claims=Actual # Claims less effects
due to tariffs &/or discounts
Est Loss=Actual Loss less effects due
to due to tariffs &/or discounts
Balance Table
Adjust RLCs
to achieve
rate level
neutrality
Approval
Process
Ensure Rate
Level Neutrality
and Acceptable
Dislocation
October 5, 2006
Casualty Actuarial Society
Special Interest Seminar on Predictive Modeling
CLEAR - How it works
Loss Cost
Data
Vehicle
Characteristics
Modelling
NVAP
Results
Loss Cost
Estimates
+
Recent
Claims
Experience
Experience
Adjustment
VINlink
Formulae
Controls and
Balancing
Review
Process
Rate
Group
Tables
October 5, 2006