Tid: 30 augusti 2018 kl 08:30 -- 09:30.

Föredragshållare: Mai Nguyen

Title: Machine Learning Algorithms for Regression Modeling in Private Insurance

Abstract: This thesis is focused on the Occupational Pension, an important part of the retiree's total pension. It is paid by private insurance companies and determined by an annuity divisor. Regression modeling of the annuity divisor is done by using the monthly paid pension as a response and a set of 24 explanatory variables e.g. the expected remaining lifetime and advance interest rate. Two machine learning algorithms, artificial neural networks (ANN) and support vector machines for regression (SVR) are considered in detail. Specifically, different transfer functions for ANN are studied as well as the possibility to improve the SVR model by incorporating a non-linear Gaussian kernel. To compare our result with prior experience of the Swedish Pensions Agency in modeling and predicting the annuity divisor, we also consider the ordinary multiple linear regression (MLR) model. Although ANN, SVR and MLR are of different nature, they demonstrate similar performance accuracy. It turns out that for our data that MLR and SVR with a linear kernel achieve the highest prediction accuracy. When performing feature selection, all methods except SVR with a Gaussian kernel encompass the features corresponding to advance interest rate and expected remaining lifetime, which according to Swedish law are main factors that determine the annuity divisor. The results of this study confirm the importance of the two main factors for accurate modeling of the annuity divisor in private insurance. We also conclude that, in addition to the methods used in previous research, methods such as ANN and SVR may be used to accurately model the annuity divisor.

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