  Insurers usually turn to generalized linear models for modelling claim
frequency and severity data. Due to their success in other fields, machine
learning techniques are gaining popularity within the actuarial toolbox. Our
paper contributes to the literature on frequency-severity insurance pricing
with machine learning via deep learning structures. We present a benchmark
study on four insurance data sets with frequency and severity targets in the
presence of multiple types of input features. We compare in detail the
performance of: a generalized linear model on binned input data, a
gradient-boosted tree model, a feed-forward neural network (FFNN), and the
combined actuarial neural network (CANN). Our CANNs combine a baseline
prediction established with a GLM and GBM, respectively, with a neural network
correction. We explain the data preprocessing steps with specific focus on the
multiple types of input features typically present in tabular insurance data
sets, such as postal codes, numeric and categorical covariates. Autoencoders
are used to embed the categorical variables into the neural network and we
explore their potential advantages in a frequency-severity setting. Finally, we
construct global surrogate models for the neural nets' frequency and severity
models. These surrogates enable the translation of the essential insights
captured by the FFNNs or CANNs to GLMs. As such, a technical tariff table
results that can easily be deployed in practice.

Machine Learning;Risk Management