Integrating core non-life actuarial activities by incorporating individual policy risk

Integrating core non-life actuarial activities by incorporating individual policy risk

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Speaker: Jurjen Boog

The actuarial cycle links the core actuarial activities of non-life insurance: pricing, reserving, risk and performance management. However, in practice little communication exists between the models in each of the individual elements. The models too often make use of different data, granularity, modelling techniques and frequency of updating. Therefore, their output is difficult to align and to compare. This results in sub-optimal pricing, reserving on aggregated data, imperfect risk quantification and steering of the business.

We integrate pricing, reserving, risk and performance management and simultaneously incorporate individual policy risk. We attain this by introducing a new type of reserve in addition to the well-known IBNR and RBNS reserve: the Not Incurred and thus Not Reported (NINR) reserve.

The NINR is the estimate of the expected future claim amount on a policy. It thus replaces the current premium reserve. Rather than traditional reserving methods it is estimated using individual policy holder risk characteristics. Moreover, the NINR can be estimated at any point in time during the policy life in order to incorporate changes in predicted future claim payments.
The methodology described above provides three main advantages:

Firstly, this approach creates enhanced insights into the linkage between portfolio risk, premium income and claim payments. This creates new possibilities to predictively estimate business opportunities from a profitability, risk and client value perspective.

Secondly, this approach allows for modelling consistently over pricing, reserving, risk and performance, where also new techniques can be easily introduced, e.g. the use of machine learning.

Thirdly, as the NINR, IBNR and RBNS are continuously modelled, the total reserve and risks are always tailored to the underlying portfolio.

In our presentation, we will elaborate on our approach introduced above and show its added value in a real-life application.

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