Application and Evaluation of Artificial Neural Networks in Solvency Capital Requirement Estimations for Insurance Products

The least squares Monte Carlo (LSMC) approach is commonly used in the estimation of the solvency capital requirement (SCR), as a more computationally efficient alternative to a full nested Monte Carlo simulation. This study compares the performance of artificial neural networks (ANNs) to that of the LSMC approach in the estimation of the SCR of various financial portfolios. More specifically, feedforward ANNs with multiple hidden layers are implemented and the results show that an ANN is superior in terms of accuracy compared to the LSMC approach. The ANN and LSMC approaches reduce the computation time to approximately 2-5% compared to a full nested Monte Carlo simulation. Significant time is however spent on validating and tuning the ANNs in order to optimise their performance. Despite continuous improvements in tools and techniques available for optimisation and visualisation of ANNs, they are to a certain degree still regarded as “black boxes”. This study employs various tools and techniques to visualise and validate the implemented ANN models as extensively as possible. Examples include software libraries such as TensorFlow and Seaborn as well as methods to prevent overfitting such as weight regularisation and dropout. These tools and techniques do indeed contribute to shedding some light on the black box. Certain aspects of ANNs are however still difficult to interpret, which might make them less manageable in practise.