Measuring per-mile risk for pay-as-you- drive automobile insurance - - PowerPoint PPT Presentation

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Measuring per-mile risk for pay-as-you- drive automobile insurance - - PowerPoint PPT Presentation

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Measuring per-mile risk for pay-as-you- drive automobile insurance Eric Minikel CAS Ratemaking & Product Management Seminar March 20, 2012 Professor Joseph Ferreira, Jr. and Eric Minikel Measuring per-mile risk for pay-as- you-drive

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SLIDE 1

Measuring per-mile risk for pay-as-you- drive automobile insurance

Eric Minikel CAS Ratemaking & Product Management Seminar March 20, 2012

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SLIDE 2

Feasibility Assessment

Professor Joseph Ferreira, Jr. and Eric Minikel “Measuring per-mile risk for pay-as- you-drive automobile insurance”

Full text of CLF report: goo.gl/exuSp or Google “CLF PAYD”

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Feasibility Assessment

Presentation Outline

  • Background
  • Datasets
  • Per-mile risk modeling
  • Equity and environmental impacts
  • Conclusions
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Feasibility Assessment

Background

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Feasibility Assessment

What is pay-as-you-drive insurance?

  • Cents-per-mile rate
  • Customers billed for actual miles driven
  • Potential benefits

– Improved actuarial accuracy – Opportunity for consumers to save money – Reduced negative externalities (congestion, accidents, pollution)

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Feasibility Assessment

Status of pay-as-you-drive insurance in U.S.

  • MileMeter offers true cents-per-mile

coverage in Texas

  • Verified low-mileage or black box discount

programs available from a variety of providers in many states

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SLIDE 7

Feasibility Assessment

Status of pay-as-you-drive insurance in U.S.

  • 50 state regulators
  • 16 prohibit PAYD

– Including Massachusetts

  • Many regulatory barriers to introduction

and adoption of PAYD

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SLIDE 8

Feasibility Assessment

Our contribution

  • Assess risk-mileage relationship with

largest disaggregate dataset to date

  • Classifies drivers by class and territory
  • Characterize rate levels and relativities
  • Model economic and environmental

impacts

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Feasibility Assessment

Dataset

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Feasibility Assessment

Data sources

Data released by Massachusetts Executive Office of Energy and Environmental Affairs (EOEEA)

  • Odometer readings from mandated annual

safety checks (Mass RMV)

  • Insurance policy and claims data from Mass

“statistical plan” reporting (Commonwealth Automobile Reinsurers)

  • Original dataset: goo.gl/la5fJ
  • Analytic dataset: goo.gl/GiVxW
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Feasibility Assessment

Data processing

  • Estimate mileage from odometer readings
  • Estimate pure premiums from losses plus
  • utstanding reserves
  • Join on VIN
  • Consider only compulsory coverage

categories and levels

  • Divide drivers into coarse rate groups (five

classes, six territories)

  • Parse VINs to obtain fuel economy

estimates

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Feasibility Assessment

Five classes

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Feasibility Assessment

Six territories

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Feasibility Assessment

Sample size

Policy year 2006:

  • 3M car-years of earned exposure

– 71% of private, insured autos in Massachusetts

  • $502M in claims
  • 34B miles
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Feasibility Assessment

Per-mile risk modeling

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Feasibility Assessment

Pure premium vs. ann. mileage (all drivers)

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Feasibility Assessment

Pure premium vs. ann. mileage (all drivers)

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Feasibility Assessment

Reasons for non-proportionality

  • All drivers are considered together
  • Regression to the mean
  • Experience and driving habits
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Feasibility Assessment

Pure premium vs. ann. mileage (all drivers)

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Feasibility Assessment

Pure premium vs. ann mileage (T3 adults)

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Feasibility Assessment

Pure premium vs. ann. mileage (T3 adults 90%+)

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Feasibility Assessment

Pure premium vs. ann. mileage (all drivers)

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Feasibility Assessment

Pure premium vs. ann mileage (T3 adults)

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Feasibility Assessment

Pure premium vs. ann. mileage (T3 adults 90%+)

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Feasibility Assessment

Regression analysis

  • Poisson regression

– Respects “rare event” nature of accidents – Allows true disaggregate analysis – Results in an exponential model of the risk- mileage relationship

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Feasibility Assessment

Poisson regression #1

Pure premium = $6.53 * (ann_miles0.36)

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Feasibility Assessment

Poisson regression #2

  • Pure premium = $2.35 * (ann_miles0.40) *

(class relativity) * (terr relativity)

  • Limitation: relativities only affect

magnitude of curve, not its shape.

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Feasibility Assessment

Poisson regression #3

  • T3 adults only
  • Pure premium=$1.70×ann_miles0.46
  • Exponent is higher for any one class-

territory group than for all class-territory groups together

  • Limitation: regression to the mean is still

present

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Feasibility Assessment

Poisson regression #4

  • T3 adults only
  • 90% or greater overlap between mileage

and policy periods–reliable mileage estimates

  • Pure premium= $0.74×ann_miles0.54
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Feasibility Assessment

Poisson regression conclusions

  • Mileage-risk relationship may be even

stronger than we observe here as industry would use:

– Finer rate groups – More rating factors – Better mileage estimates

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Feasibility Assessment

Poisson regression conclusions

  • Mileage and risk are strongly correlated
  • Relationship becomes stronger and more

nearly proportional when controlling for class, territory and RTM.

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Feasibility Assessment

Regression analysis

  • Linear regression

– Shows how much of variation is explained by different factors – Results in a flat rate plus cents-per-mile model, a more realistic model of how PAYD might be priced

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Feasibility Assessment

Linear regression

Factors Adjusted R2 Mileage .09 Class and territory .57 Mileage, class and territory .72

  • Vehicles aggregated into “bins” by class,

territory and 500-mile annual mileage range; weighted by number of vehicles

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Feasibility Assessment

Linear regression conclusions

  • The whole is better than the sum of the

parts

– .72 > .09 + .57 – Mileage is a better predictor of risk when paired with some control (class and territory)

  • n where and how miles are being driven
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Feasibility Assessment

Per-mile risk assessment conclusions

  • Mileage is correlated with risk
  • Correlation is stronger with class-territory

control

  • PAYD could be priced with individual per

mile rates based on class and territory

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Feasibility Assessment

Equity and environmental impacts

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Feasibility Assessment

VMT reduction model

  • Model consumer response to increase in

marginal cost of driving a mile due to PAYD

  • Modeled for each individual vehicle based
  • n its annual mileage, fuel economy and

insurance rate group

  • Constant elasticity of -0.15 assumed
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Feasibility Assessment

VMT reduction model

  • Results–if all MA drivers adopted PAYD:

– 9.5% aggregate VMT reduction if pricing is strictly per mile, – 5.0% if a flat fee covers first 2000 miles, with a lower per mile fee thereafter

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Feasibility Assessment

Fairness and equity impacts

Assumption: PAYD would be offered as a consumer option Key findings:

  • No geographic impacts
  • Cross-subsidy alleviated
  • Congestion and safety benefits
  • Controllable individual factors improve

fairness

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Feasibility Assessment

Conclusions

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Feasibility Assessment

Summary of key findings

  • PAYD is actuarially justified
  • PAYD is equitable and fair
  • Statewide adoption would result in VMT

reductions of 5 – 9.5%

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Feasibility Assessment

Policy implications

  • Regulators should support PAYD
  • Consumer protections needed for:

– Consumer awareness – Uninsured driving – ‘Tracking data’

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Feasibility Assessment

Eric Minikel <eric.minikel@ibigroup.com> Professor Joseph Ferreira <jf@mit.edu> Special thanks to:

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