An individual claims reserving model for reported claims
The article presents a claims reserving technique that uses claim-specific feature and past payment information in order to estimate claims reserves for individual reported claims. The author focuses on modeling RBNS (reported but not settled) reserves, which consist of estimated expected future payments for claims that have already been reported. A single neural network in designed, which allows to estimate expected future cash flows for every individual reported claim. A consistent way of using dropout layers in introduced in order to fit the neural network to the incomplete time series of past individual claims payment. The author mainly uses synthetic insurance data, for the sake of reproducibility. This analysis is complemented by an application on real Swiss accident insurance data. The synthetic data is generated from the individual claims history simulation machine. For every individual reported claim, there is a claim-specific feature information such as age or labor sector of the insured. After a claim gets reported, it can trigger multiple (yearly) payments. The time lags (on a yearly scale) between payments and reporting date are called payment delays. At the end of the last considered accident year, for every reported claim we have claim specific feature information and past payment information. The main goal is to use this information in order to estimate expected future payments for every individual reported claim. This corresponds to estimating RBNS reserves.
scientific article; zbMATH DE number 7457623