Insurance modelling

The estimates of future cash flows should incorporate all reasonable and supportable information available without undue cost or effort about amount, timing and uncertainty of those future cash flows. To accomplish this, an entity should estimate the expected value of the full range of possible outcomes. Estimates and assumptions should be unbiased (that is, neither conservative nor optimistic).

The objective of considering the full range of all possible outcomes is to incorporate all reasonable and supportable information. An insurer is not required to identify every possible scenario. Explicit scenarios are not required if the result meets the objective. However, a single scenario based on the most likely outcome or the more-likely-than-not outcome would not meet the objective where there is a non-linear relationship between the different scenarios and the associated changes in measurement. Judgement is required to determine the appropriate number of scenarios that will capture material non-linearity. This will depend on facts and circumstances and should be periodically reassessed.

Example – Stochastic and deterministic modelling

The table below describes an insurance contract under a range of scenarios that reflect all possible outcomes. The table summarises information about net cash inflows and the probability of each scenario:


Net cash inflows/(outflows), CU


Probability-weighted outcome, CU



















Under IFRS 4, entities use either stochastic or deterministic modelling for measurement of insurance liabilities. Stochastic modelling requires considering various scenarios in determining the value of the insurance liabilities. Deterministic modelling usually identifies the most likely outcome or more-likely-than-not outcome and is not based on a range of all possible outcomes. For this example, the value of the insurance liability determined using stochastic modelling is CU10,800,000 (that is, probability-weighted outcome), while using deterministic modelling the value is CU15,000,000 (that is, most likely outcome).

Unlike many current accounting models that develop a single ‘best estimate’, under IFRS 17 all scenarios and their associated probabilities (even remote ones) should be considered and weighted. However, not all cases will require the development of explicit scenarios. In cases where there are complex underlying factors that behave in a non-linear fashion, sophisticated stochastic modelling might be needed. This could happen, for example, if the cash flows reflect a series of interrelated options. The objective is to incorporate all of the relevant information and not ignore any information that is difficult to obtain.

Stochastic modelling can be complicated, both to initially implement and to maintain. This may be an additional IFRS 17 implementation complexity for entities that do not use stochastic modelling currently under IFRS 4.

Reasonable and supportable information is defined as information reasonably available at the reporting date without undue cost or effort. Uncertainty and judgement associated with available information does not necessarily mean that information is not reasonable and supportable. Information available without undue cost and effort will include an entity’s own internal information, such as historical claims, benefits and lapse data and any forecasts of potential future claims, benefits and lapses, as well as externally available information such as economist forecasts and statistics (for example, mortality information) for a country where the entity operates. Insurance modelling

The following are examples of possible sources of information about probabilities, amounts and timing of future payments: Insurance modelling

  • actual information available about policyholders, such as claims already reported; Insurance modelling
  • an entity’s own historical experience, such as claims previously reported for similar contracts; Insurance modelling
  • country or industry information about historical experience, such as country mortality rates; Insurance modelling
  • information about emerging trends or changes in economic, demographic and other conditions, such as development of a treatment for diseases that impact mortality rates; and
  • changes in an entity’s own procedures that might affect the way in which information is gathered and presented, such as gathering sufficient statistically credible data for new products that enable an entity to measure liabilities using its own statistics while previously that was not possible. Insurance modelling

Leave a Reply

Your email address will not be published. Required fields are marked *