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Chapter 12 - Projecting Exposure Values

vidhya36

vidhya36

Very Active Member
In Chapter 12, Page 20, under the heading "Projecting Exposure Values", I read -

"For example, a premium rate of £2 per mille set in 1980 might still be appropriate in 2020. But, we very much doubt whether a premium of £15 per vehicle-year in 1980 would still be acceptable in 2020!"

Can somebody help me with the essence of this statement, I am missing something to comprehend this fully.

Thanks
 
A premium rate per mille, is a premium rate per £1,000 of sum insured.

Hence, if the exposure measure (sum insured in this case) inflates at the same rate as the average claim amounts, you should not need to amend the premium rate per mille.

However, where the premium rate is a monetary amount per policy (eg £x per vehicle year), that will definitely need increasing for the effect of claims inflation over time.
 
Darren Michaels said:
A premium rate per mille, is a premium rate per £1,000 of sum insured.

Hence, if the exposure measure (sum insured in this case) inflates at the same rate as the average claim amounts, you should not need to amend the premium rate per mille.

However, where the premium rate is a monetary amount per policy (eg £x per vehicle year), that will definitely need increasing for the effect of claims inflation over time.
Click to expand...

Hi Darren, is Turnover in $ a special case of being exempt from inflating into the period to be rated? I see that in April 2017, Question 10 the Turnover is calculated for each AY (from growth from the prior AY), but then left alone as a "raw" value during that AY.
 
When you're pricing, you're basically looking to put everything (exposure, claims, premiums or whatever) on an 'as-if' basis. This generally means adjusting past data for trends and other inflationary increases. But whether you need to make any adjustments in a particular exam question would depend on the data you've been given, the assumptions you make, and what you are trying to achieve. Hope that makes sense. The important thing is that you consider the factor and do something sensible, stating your assumptions. There are plenty of past questions that test your understanding of the difference between AY data and UY data, so watch out for those.
 
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