Chapter 16: Stochastic Reserving Methods Flashcards
9 Random factors influencing the run-off of claims reserves
(sources of process error)
We can consider the run-off of claims reserves to be a random process, with many random factors influencing the outcome. These uncertain factors include:
1. the occurrence and severity of claims
2. the notification delays on individual claims
3. legal changes that affect the size of awards
4. legal changes that affect the ‘heads of damage’ awarded. This can change the types of loss recognised in compensation awards for serious injuries, for example loss of income, medical and nursing costs
5. changes in the litigiousness of society
6. levels of claims inflation which is often related to levels of price inflation and wage inflation in the economy
7. court rulings on liability or quantum of individual claims not foreseen by claims handlers and/or not in the historical data
8. changes in the mix of claim types, either caused by an underlying change in claim type experience or by changes in the mix of business written
9. changes in claims handling, either because of policy changes or because of external events, for example a catastrophe leading to claims handlers being over-stretched
10. the emergence of new types of claim
changes in the way claims are settled, for example if more claims are settled in the form of a series of payments rather than as lump sums (in the UK this is referred to as a PPO).
These factors contribute to the uncertainty underlying the process of the run-off of claims.
“Heads of damage”
Types of loss recognised in compensation awards for serious injuries, such as loss of income, medical and nursing costs, etc.
Further uncertainties in using historic data to project the run-off of claims (3)
- The historic data only provides a limited sample
- The quality of data may have varied over time.
- “model uncertainty” because there are many ways of deriving the reserve estimates and many judgements are required.
4 Terms used to identify the sources of uncertainty
- Parameter uncertainty - estimation error
- Process uncertainty - inherent random noise in the process
- Model error - choice/specification of model
- Systemic error - data selection error
PREDICTION ERROR OR STANDARD ERROR = PARAM/ESTIMATION ERROR + PROCESS ERROR
Process uncertainty
The uncertainty in what the future outcome will be.
This is the randomness of the underlying process.
Parameter uncertainty
The uncertainty in selecting parameters within the reserving process, and hence the results.
Model error
The error/uncertainty arising from the fact that we might select an inappropriate model to derive our reserve estimates.
Systemic error
The uncertainty arising from unforeseen trends or shifts away from the current claims environment.
Stochastic claims reserving can be used to: (6)
- assess reserve adequacy
- Compare the reasonableness of different sets of reserve estimates.
- Compare datasets at different as at dates.
- monitor performance of claims
- inform management so that decisions to contract or expand business is taken.
- allocate capital
- provide information to investors
- facilitate discussions with regulators
- Price insurance and reinsurance policies
3 Main benefits of using a stochastic approach for reserving
- We can estimate the RELIABILITY OF FITTED MODEL, and likely the MAGNITUDE OF RANDOM VARIATION
- We may apply STATISTICAL TESTS TO VERIFY ASSMPTIONS and gain understanding of the variability of the claims process.
- We can develop models in which the influence of each data point in determining the fitted model depends on the amount of random variation within that data point.
5 Drawbacks to stochastic reserving
- It takes more time
- It requires a higher level of skill and training
- The methods are more complicated, so the risk of mistakes is greater and they are harder to explain to a non-technical audience
- A considerable element of judgement is required in the choice of model and in selecting a prior (Bayesian methods)
- Using more sophisticated methods may lead to spurious accuracy and false confidence in the results.
3 ways in which the appropriateness of any model might be tested
- Examine plots or triangles of residuals
- Use F tests to check the appropriateness of the number of parameters.
- Fit the model to past data
3 Types of stochastic claims reserving models
- analytical methods - Mack, ODP, negative binomial, normal appox to negative binomial, log normal
- simulation methods - ODP, bootstrapped form
- Bayesian methods - BF method, Bayesian form
3 relative merits of stochastic and deterministic approaches
- Deterministic approaches only consider a limited number of factors and one result from each, while a stochastic model generates a number of potential scenarios that may not be thought of under a deterministic approach.
- Failure is often due to the interaction of many differing factors which could not be modelled deterministically. The stochastic model can allow for the interdependency of these key factors.
- Analysis of the impact of atypical scenarios aids understanding of variation around expected outcomes, and assigns a distinct value to them.
Define “reserve risk”
The risk in respect of financial losses that could arise if the actual claim payments from expired business turn out to be higher than reserved for.