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.

使用深度学习结构对频率和严重性数据进行机器学习的保险定价研究，将广义线性模型与神经网络结合，在多种输入特征存在的情况下进行性能比较并构建全局替代模型，为实际部署提供技术性费率表。

基于频率和严重程度数据的保险定价神经网络：从数据预处理到技术费率的基准研究

Neural networks for insurance pricing with frequency and severity data:  a benchmark study from data preprocessing to technical tariff

The development of Machine Learning is experiencing growing interest from the
general public, and in recent years there have been numerous press articles
questioning its objectivity: racism, sexism, \dots Driven by the growing
attention of regulators on the ethical use of data in insurance, the actuarial
community must rethink pricing and risk selection practices for fairer
insurance. Equity is a philosophy concept that has many different definitions
in every jurisdiction that influence each other without currently reaching
consensus. In Europe, the Charter of Fundamental Rights defines guidelines on
discrimination, and the use of sensitive personal data in algorithms is
regulated. If the simple removal of the protected variables prevents any
so-called `direct' discrimination, models are still able to `indirectly'
discriminate between individuals thanks to latent interactions between
variables, which bring better performance (and therefore a better
quantification of risk, segmentation of prices, and so on). After introducing
the key concepts related to discrimination, we illustrate the complexity of
quantifying them. We then propose an innovative method, not yet met in the
literature, to reduce the risks of indirect discrimination thanks to
mathematical concepts of linear algebra. This technique is illustrated in a
concrete case of risk selection in life insurance, demonstrating its simplicity
of use and its promising performance.

机器学习在伦理学、保险定价和风险选择方面面临着人们日益关注的议题，本研究探讨了间接性歧视的风险，并提出了一种基于线性代数的创新方法来降低间接性歧视的风险，以提高保险的公平性和性能表现。