CAS Actuarial Modeling and Pricing Techniques Flashcards
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What is the 'pure premium' method for calculating insurance rates?
Answer: Dividing projected ultimate losses by the number of exposures to derive the average loss cost per exposure unit
The pure premium method directly derives a loss cost per exposure unit, which is then loaded for expenses and profit to produce the final rate.
In actuarial ratemaking, what does 'on-leveling' premium data accomplish?
Answer: Adjusting historical premium to reflect what it would have been at current rate levels
On-leveling (or bringing premiums to current level) ensures historical premiums are comparable to current rates before computing historical loss ratios.
What is the 'loss development factor' (LDF) used for in casualty actuarial work?
Answer: To project reported or paid losses at an interim age to their ultimate settled value
Loss development factors (also called age-to-age factors) are derived from historical loss triangles and applied to immature losses to estimate ultimate claim costs.
Which of the following best describes the Bornhuetter-Ferguson (BF) reserve method?
Answer: A weighted blend of the development method and the expected loss method, giving credibility to both reported losses and an a priori expected loss estimate
The BF method is valuable for immature accident years where little data exists, balancing observed development patterns with an independent a priori loss estimate.
In GLM (Generalized Linear Model) ratemaking, what is the typical link function used for multiplicative rating plans?
Answer: Log link function
The log link function produces a multiplicative rating structure where rating factors for different variables multiply together, which is standard in P&C insurance pricing.
What is 'credibility theory' used for in casualty actuarial pricing?
Answer: To blend an insured's own experience with external data in proportion to the statistical reliability of the insured's data
Credibility theory assigns a weight Z to an insured's own data (0 ≤ Z ≤ 1) based on its volume and homogeneity, blending it with broader complement-of-credibility data.