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Actuarial Modelling of Claim Counts Risk Classification, Credibility ...

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xviii<br />

<strong>Actuarial</strong> <strong>Modelling</strong> <strong>of</strong> <strong>Claim</strong> <strong>Counts</strong><br />

are available under most statistical packages to estimate the regression parameters by<br />

maximum likelihood.<br />

Pay-As-You-Drive System<br />

Every kilometer travelled by a vehicle transfers risk to its insurer: the total cost <strong>of</strong> the<br />

coverage thus increases kilometer by kilometer. This is why several authors, including<br />

Butler (1993) suggested charging a cents-per-kilometer based premium; the car-kilometer<br />

should be adopted as the exposure unit instead <strong>of</strong> the car-year that is currently used. Motor<br />

insurance companies are adopting a new scheme called ‘pay as you drive’ (henceforth<br />

referred to as PAYD for the sake <strong>of</strong> brevity). Under a PAYD system, a driver pays for every<br />

kilometer driven at a rate varying from a premium to use the busiest roads at peak hours to<br />

a lower rate for the rural roads.<br />

Several insurance companies (including the pioneering company Norwich Union,<br />

http://www.norwichunion.com/pay-as-you-drive/) have now started to <strong>of</strong>fer a motor<br />

insurance policy under a PAYD system after successful pilot schemes involving thousands <strong>of</strong><br />

motorists. With PAYD systems, drivers are provided with in-car Global Positioning System<br />

(GPS) devices coupled with maps, enabling the insurance company to calculate insurance<br />

premiums for each journey, depending on time <strong>of</strong> day, type <strong>of</strong> road and distance travelled.<br />

A ‘black box’ is installed in the car and receives signals from GPS technology to determine<br />

the vehicle’s current position, speed, and time and direction driven. The black box then<br />

acts as a wireless modem to transmit these inputs through standard mobile phone networks<br />

to the insurer. The insurer sends a monthly bill to the customer based on vehicle usage,<br />

including time <strong>of</strong> day, type <strong>of</strong> road and distance travelled. Historical data then provide<br />

detailed information <strong>of</strong> how, when and where cars are actually used, and whether accidents<br />

and claims can be identified with particular factors. Moreover, the tracker detects speed<br />

infringements and more generally, the aggressiveness behind the wheel. Dangerous driving<br />

habits could lead to higher premiums for car insurance, increasing road safety. In addition<br />

to the static measures <strong>of</strong> risk, such as the driver’s age, dynamic measures, such as speed,<br />

time <strong>of</strong> day, and location, are used to give the best possible overall risk assessment.<br />

The generalization <strong>of</strong> the PAYD system is also expected to change motorists’ attitudes:<br />

like petrol, insurance is bought on a pay-as-you-drive basis, and people think <strong>of</strong> their<br />

insurance costs as related to their actual use <strong>of</strong> their vehicle. Several North-American studies<br />

demonstrate that PAYD systems could reduce motoring by more than 10 %. The PAYD<br />

rating system is expected to decrease congestion and pollution (since the busier roads usually<br />

attract the higher rates).<br />

Experience Rating<br />

The trend towards more classification factors has lead the supervising authorities to exclude<br />

from the tariff structure certain risk factors, even though they may be significantly correlated<br />

to losses. Many states consider banning classification based on items that are beyond the<br />

control <strong>of</strong> the insured, such as gender or age. The resulting inadequacies <strong>of</strong> the a priori<br />

rating system can be corrected for by using the past number <strong>of</strong> claims to reevaluate future<br />

premiums. This is much in line with the concept <strong>of</strong> fairness: as it will be seen from Chapter 2,<br />

a priori ratemaking penalizes individuals who ‘look like’ bad drivers (even if they are in

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