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AFIR-ERM Webinar: European ESG template for insurance regulations IDD and SFDR

Join us in our upcoming webinar on 


May 6, 2022
 5:00 am - 6:30 am (EDT)
11:00 am - 12:30 pm (CET)




The Lee-Carter model has become a benchmark in stochastic mortality modeling. However, its forecasting performance can be significantly improved upon by modern machine learning techniques. We propose a convolutional neural network (NN) architecture for mortality rate forecasting, empirically compare this model as well as other NN models to the Lee-Carter model and find that lower forecast errors are achievable for many countries in the Human Mortality Database. We provide details on the errors and forecasts of our model to make it more understandable and, thus, more trustworthy. As NN by default only yield point estimates, previous works applying them to mortality modeling have not investigated prediction uncertainty. We address this gap in the literature by implementing a bootstrapping-based technique and demonstrate that it yields highly reliable prediction intervals for our NN model.



Ghislain Perisse

Ghislain Perisse is working for Fidelity International, as Head of Insurance Solutions, Europe. He has worked at Credit Lyonnais, Societe Generale, Merrill Lynch and Morgan Stanley, and then at AXA IM as Head of Insurance Solutions BD, and most recently at Schroders as Head of Insurance Strategy. In parallel, within the European Working group and now FinDatEx, he initiated the different Regulatory templates (TPT, EMT, EPT, EFT and EET).


Register now!