Determining incurred but not reported (IBNR) reserves using a collective risk model framework for a general insurance business line in a Kenyan insurance company
General Insurance companies set up reserves in order to meet future claim liabilities. Reasonable forecasting of these liabilities is therefore an integral part of an insurer's business. Actuarial methods such as the chain ladder method have long been used to estimate insurer liabilities. Stochastic improvements of the chain ladder method mhave also been developed in order to obtain a standard error around a point estimate and a full distribution in some instances. However, such methods depend on heavily aggregated data in run-off triangles. Such an aggregation leads to loss of potentially predictive information. This paper uses specific claim information such as reporting delay and size of individual claims to forecast claim liabilities. It uses the collective risk model to combine expected claim size and frequency of claims. The data-set used in modelling is a realistic liability business from a Kenyan insurer. A final comparison of existing methods (Mack and Over-Dispersed Poisson) and the collective risk model is done for validation purposes. From the case-study findings, the collective risk model was preferred over traditional stochastic methods since it had a lower predictive error and was more realistic in modelling the claim process.