Indirect discrimination and fairness are major concerns in algorithmic models. This is particularly true in insurance, where protected policyholder attributes are not allowed to be used for insurance pricing. Simply disregarding protected policyholder attributes is not an appropriate solution,...

A very popular model-agnostic technique for explaining predictive models is the SHapley Additive exPlanation (SHAP). There is a conditional version and an unconditional one of SHAP, the latter is also known as interventional SHAP. Except for tree-based methods, usually the unconditional version...

Deep neural networks have become an important tool for use in actuarial tasks, due to the significant gains in accuracy provided by these techniques compared to traditional methods, but also due to the close connection of these models to the Generalized Linear Models (GLMs) currently used in...

The present manuscript provides a basis in non-life insurance mathematics and statistics which form a core subject of actuarial science. It discusses collective risk modeling, individual claim size modeling, approximations for compound distributions, ruin theory, premium calculation principles,...

Basis risk is an important consideration when hedging longevity risk with instruments based on longevity indices, since the longevity experience of the hedged exposure may differ from that of the index. As a result, any decision to execute an index-based hedge requires a framework for (1)...

Deep Learning models are currently being introduced into business processes to support decision-making in insurance companies. At the same time model risk is recognized as an increasingly relevant field within the management of operational risk that tries to mitigate the risk of poor business...

In this tutorial we introduce recurrent neural networks (RNNs), and we describe the two most popular RNN architectures. These are the long short-term memory (LSTM) network and gated recurrent unit (GRU) network. Their common field of application is time series modeling, and we demonstrate their...

This tutorial studies unsupervised learning methods. Unsupervised learning methods are techniques that aim at reducing the dimension of data (covariables, features), cluster cases with similar features, and graphically illustrate high dimensional data. These techniques do not consider response...

The purpose of this paper is to identify a workhorse mortality model for the adult age range (i.e., excluding the accident hump and younger ages). It applies the “general procedure” (GP) of Hunt and Blake (2014) to identify an age-period model that fits the data well before adding in a...