Efficient and Reliable Solvency II Loss Estimates With Deep Neural Networks

The Solvency II Directive from 2009 requires from life insurance companies to derive the full probability distribution forecast for one-year losses. Since no analytical formula for one-year losses exists and a full Monte Carlo nested calculation is computationally infeasible, life insurers utilize either the Standard Formula or a proxy methodology. Using the ideas from Least-Squares Monte Carlo proxy method, we explain how neural networks can be used to reliably predict the one-year losses opening the door to finally use risk models for other important value generating applications like asset liability management, strategic asset allocation and product strategies.