This paper focuses on the problem of automatically forecasting mortality rates over long time horizons. In the context of the Lee-Carter model, an approach based on general regression neural networks is presented and discussed. Our proposal preserves the LC defining parameters and structure, adds flexibility at reduced costs in terms of complexity, and requires a weak human intervention for the identification of the optimal parameters. Moreover, GRNN models need relatively few data to train, an advantage useful in actuarial data. An application to real data shows that an additive GRNN model has, in general, better forecasting performances than both the multiplicative GRNN model and the KNN model, taken as a benchmark. Furthermore, between two different long term forecasting strategies, the analysis highlights how, in general, MIMO is preferable to the classic recursive procedure.
