2.1. Setting and Notation

We first introduce some notation to formalise the problem discussed above.

We write Y to denote the response variable, which is typically one of claim frequency, claim severity, or risk premium for most insurance problems. We assume that the distribution of $Y \in \mathcal{Y}$ depends on some covariates or features. In this work, we draw a distinction between standard features $\textbf{x} \in \mathcal{X}$ (i.e., numeric features and low-dimensional categorical features) and the high-cardinality categorical features $\textbf{z} \in \mathcal{Z}$ consisting of K distinct features each with $q_i\;(i = 1, \cdots, K)$ categories. The latter is the subject of our interest. To clarify, while $\textbf{z}$ represents the collection of K categorical features, its dimensional space when one-hot encoded is represented as $ \mathbb{R}^q $ , where $ q = \sum_{i=1}^K q_i $ .

We assume $\textbf{x} \in \mathbb{R}^p$ and $\textbf{z} \in \mathbb{R}^q$ , where $p \in \mathbb{Z}$ and $q \in \mathbb{Z}$ represent the respective dimensions of the vectors. The empirical observations (i.e., data) are denoted $\mathcal{D}=(y_i, \textbf{x}_i, \textbf{z}_i)_{i=1}^n$ , where n is the number of observations in the dataset. For convenience, let us also write $\textbf{y}=[y_1, y_2, \cdots, y_n]^\top \in \mathbb{R}^n,$ $\textbf{X} = [\textbf{x}_1, \textbf{x}_2, \cdots, \textbf{x}_n]^\top \in \mathbb{R}^{n \times p}$ and $\textbf{Z} = [\textbf{z}_1, \textbf{z}_2, \cdots, \textbf{z}_n]^\top \in \mathbb{R}^{n \times q}$ . In general, we use bold font to denote vectors and matrices.

The purpose of any predictive model is to learn the conditional distribution $p(y|\textbf{x}, \textbf{z})$ . It is generally assumed that the dependence of the response Y on the covariates is through a true but unknown function $\mu:(\mathcal{X}, \mathcal{Z}) \to \mathcal{Y}$ , such that

\begin{align*}p(y|\textbf{x}, \textbf{z}) = p(y|\mu(\textbf{x}, \textbf{z}), \boldsymbol{\phi}),\end{align*}

where $\boldsymbol{\phi}$ represents a vector of any additional or auxiliary parameters of the likelihood. Most commonly $\mu(\textbf{x}, \textbf{z})$ will be the conditional mean $\mathbb{E}(Y|\textbf{x}, \textbf{z})$ , although it can be interpreted more generally. The predictive model then approximates $\mu(\!\cdot\!)$ by some parametric function $\hat{\mu}(\!\cdot\!)$ . We write $\boldsymbol{\beta}$ to denote the collection of model parameters. Specifically, a linear regression model will assume a linear structure for $\mu(\!\cdot\!)$ with $\boldsymbol{\phi} = \sigma^2_\epsilon$ to account for the error variance. Indeed, for most ML models, the function $\mu(\!\cdot\!)$ is the sole subject of interest (as opposed to the entire distribution $p(y|\textbf{x}, \textbf{z})$ ). There are many different options for choosing the function $\mu(\!\cdot\!)$ . We discuss and compare the main ones in this paper; see also Section 3.1.

In what follows, for notational simplicity we will now assume that $\textbf{z}$ consists of only one (high-cardinality) categorical feature, that is, $K = 1$ and $q = q_1$ . The extension to multiple categorical features, however, is very straightforward; see also Section 2.3.2.

2.2. Generalised Linear Mixed Models

Mixed models were originally introduced to model multiple correlated measurements on the same subject (e.g., longitudinal data), but they also offer an elegant solution to the modelling of high-cardinality categorical features (Frees, Reference Frees and Frees2014). We now give a brief review of mixed models in the latter context. For further details, the books by McCulloch and Searle (Reference McCulloch and Searle2001), Gelman and Hill (Reference Gelman and Hill2007) and Denuit et al. (Reference Denuit, Hainaut and Trufin2019) are all excellent references for this topic.

Let us start with the simplest linear model case. A one-hot encoded linear model assumes

(2.1) \begin{align}y | \textbf{x}, \textbf{z} \sim \mathcal{N}(\mu(\textbf{x}, \textbf{z}), \sigma_\epsilon^2), \text{ with } \mu(\textbf{x}, \textbf{z}) = \beta_0 + \textbf{x}^\top \boldsymbol{\beta} + \textbf{z}^\top \boldsymbol{\alpha}, \end{align}

where $\textbf{x}$ are standard features (e.g., sum insured, age), $\textbf{z}$ is a q-dimensional binary vector representation of a high-cardinality categorical variable (e.g., occupation), $\beta_0 \in \mathbb{R}$ , $\boldsymbol{\beta} = [\beta_1, \beta_2, \cdots, \beta_p]^\top \in \mathbb{R}^p$ , and $\boldsymbol{\alpha} = [\alpha_1, \alpha_2, \cdots, \alpha_q]^\top \in \mathbb{R}^q$ with $\sum_{i=1}^q \alpha_i = 0$ are the regression coefficients to be estimated. Note that the additional constraint $\sum_{i=1}^q \alpha_i = 0$ is required to restore the identifiability of parameters.

In high-cardinality settings, the vector $\textbf{z}$ becomes high-dimensional, and hence so must be $\boldsymbol{\alpha}$ . High cardinality therefore introduces a large number of parameters to the estimation problem. Moreover, in most insurance problems, the distribution of categories will be highly imbalanced, meaning that some categories will have much more exposure (i.e., data) than others. The rarely observed categories are likely to produce highly variable parameter estimates. As established in earlier sections, all this makes it extremely difficult to estimate the $\boldsymbol{\alpha}$ accurately (Gelman and Hill, Reference Gelman and Hill2007).

Equation (2.1) is also referred to as the no pooling model by Antonio and Zhang (Reference Antonio, Zhang and Frees2014), as each $\alpha_i$ , $i = 1, 2, \cdots, q$ has to be learned independently. At the other end of the spectrum, there is a complete pooling model, which simply ignores the $\textbf{z}$ and assumes

(2.2) \begin{align}\mu(\textbf{x}, \textbf{z}) = \beta_0 + \textbf{x}^\top \boldsymbol{\beta}. \end{align}

In the middle ground between the overly noisy estimates produced by (2.1) and the over-simplistic estimates from (2.2), we can find the (linear) mixed models, which assume

(2.3) \begin{align}\mu(\textbf{x}, \textbf{z}) = \beta_0 + \textbf{x}^\top \boldsymbol{\beta} + \textbf{z}^\top \textbf{u}, \end{align}

where $\textbf{u} = [u_1, \cdots, u_q]^\top$ with $u_i \stackrel{iid}{\sim} \mathcal{N}(0, \sigma_u^2)$ are known as the random effects characterising the deviation of individual categories from the population mean, in contrast with the fixed effects $\beta_0$ and $\boldsymbol{\beta}$ . Model (2.3) is also known as a random intercept model.

The central idea in (2.3) is that instead of assuming some fixed coefficient for each category, we assume that the effects of individual categories come from a distribution, so we only need to estimate (far fewer) parameters that govern the distribution of random effects rather than learning an independent parameter per category. This produces the equivalent effect of partial pooling (Gelman and Hill, Reference Gelman and Hill2007): categories with a smaller number of observations will have weaker influences on parameter estimates, and extreme values get regularised towards the collective mean (modelled by the fixed effects, i.e., $\beta_0 + \textbf{x}^\top \boldsymbol{\beta}$ ).

In the same way that linear mixed models extend linear models, GLMMs extend GLMs by adding random effects capturing between-category variation to complement the fixed effects in the linear predictor.

Suppose that we have q categories in the data, each with $n_i\;(i = 1,2, \cdots, q)$ observations (so the total number of observations is $n = \sum_{i=1}^q n_i$ ). A GLMM is then defined by four components:

1. (1) Unconditional distribution of random effects. We assume that the random effects $u_j \stackrel{iid}{\sim} f(u_j|\boldsymbol{\gamma}), \;j= 1, 2, \cdots, q$ depend on a small set of parameters $\boldsymbol{\gamma}$ . It is typically assumed that $u_j$ follows a Gaussian distribution with zero mean and variance $\sigma_u^2$ , that is, $u_j \stackrel{iid}{\sim} \mathcal{N}(0, \sigma_u^2)$ .

2. (2) Response distribution. GLMMs assume that the responses $Y_i, i = 1, 2, \cdots, n$ , given the random effect $u_{j[i]}$ for the category it belongs to (where j[i] means that the i-th observation belongs to the j-th category, $j = 1, 2, \cdots, q$ ), are conditionally independent with an ED distribution, that is,

   (2.4) \begin{align} p\left(y_{i} | u_{j[i]}, \theta_{i}, \phi \right)= \exp \left[\frac{y_{i} \theta_{i}- b\left(\theta_{i}\right)}{\phi}+c\left(y_{i}, \phi\right)\right], \end{align}
   where $\theta_{i}$ denotes the location parameter and $\phi$ the dispersion parameter for the ED density, and $b(\!\cdot\!)$ and $c(\!\cdot\!)$ are known functions. It follows from the properties of ED family distributions that
   (2.5) \begin{align} \mu_{i} & = \mathbb{E}(Y_{i} | u_{j[i]}) = b'(\theta_{i}), \\[5pt] \operatorname{Var}(Y_{i} | u_{j[i]}) &= \phi b''(\theta_{i}) = \phi V(\mu_{i}), \nonumber \end{align}
   where $b'(\!\cdot\!)$ and $b''(\!\cdot\!)$ denote the first and second derivatives of $b(\!\cdot\!)$ and $V(\!\cdot\!)$ is commonly referred to as the variance function. Equation (2.5) implies that the conditional distribution of $Y_{i} | u_{j[i]}$ in (2.4) is completely specified by the conditional mean $\mu_{i} = \mathbb{E}(Y_{i} | u_{j[i]})$ and the dispersion parameter $\phi$ .

3. (3) Linear predictor. The linear predictor includes both fixed and random effects:

   (2.6) \begin{align} \eta_{i} = \beta_0 + \textbf{x}_{i}^\top \boldsymbol{\beta} + \textbf{z}_{i}^\top \textbf{u}, \;\; i = 1, \cdots, n, \end{align}
   where $\textbf{x}_{i}$ denotes a vector of fixed effects variables (e.g., sum insured, age) and $\textbf{z}_{i}$ a vector of random effects variables (e.g., a binary representation of a high-cardinality feature such as injury code).

4. (4) Link function. Finally, a monotone differentiable function $g(\!\cdot\!)$ links the conditional mean $\mu_{i}$ with the linear predictor $\eta_{i}$ :

   \begin{align*} g(\mu_{i}) =g(\mathbb{E}[Y_{i}|u_{j[i]}]) = \eta_{i}, \;\; i = 1, \cdots, n, \end{align*}
   completing the model.

As explained earlier, the addition of random effects allows GLMMs to account for correlation within the same category, without overfitting to an individual category as is the case of a one-hot encoded GLM. The shared parameters that govern the distribution of random effects essentially enable information to transfer between categories, which is helpful for learning.

2.3. Model Architecture

In Section 1.1, we briefly reviewed previous attempts at embedding ML into mixed models. In particular, the LMMNN structure proposed by Simchoni and Rosset (Reference Simchoni and Rosset2022) and the network proposed by Mandel et al. (Reference Mandel, Ghosh and Barnett2022) are the closest to what we envision for a mixed effects neural network. However, LMMNN suffers from two major shortcomings that restrict its applicability to insurance contexts: first, it assumes a Gaussian response with an identity link function, for which analytical results can be obtained, but which is ill suited to modelling skewed, heavier-tailed distributions that dominate insurance and financial data. Second, LMMNNs model the random effects in a non-linear way (through a subnetwork in the structure). This complicates the interpretation of random effects, which carries practical significance in many applications. In contrast, the mixed neural network proposed by Mandel et al. (Reference Mandel, Ghosh and Barnett2022) uses a computationally less efficient estimation methodology, which limits its application to moderately sized datasets; see Section 2.4.

The GLMMNet aims to address these concerns. The architectural skeleton of the model is depicted in Figure 1. We adopt a very similar structure to that of Simchoni and Rosset (Reference Simchoni and Rosset2022), except that we remove the subnetwork that they used to learn a non-linear function of $\textbf{Z}$ . As noted earlier, the main purpose of our modification is to retain the interpretability of random effect predictions from the GLMM’s linear predictor. In addition, we also want to avoid an over-complicated structure for the random effects, whose role is to act as a regulariser (Gelman and Hill, Reference Gelman and Hill2007).

Figure 1.

Architecture of GLMMNet.

In mathematical terms, we assume that the conditional $\textbf{y}|\textbf{u}$ follows an ED family distribution, with

\begin{align*}g(\boldsymbol{\mu})=g(\mathbb{E}[\textbf{y}| \textbf{u}])=f(\textbf{X})+\textbf{Z}\textbf{u}, \quad \textbf{u} \sim \mathcal{N}(\textbf{0}, \boldsymbol{\Sigma}),\end{align*}

where $g(\!\cdot\!)$ is the link function and $f(\!\cdot\!)$ is learned through a neural network.

In the following, we give a detailed description of each component in the architecture plotted above, followed by a discussion on how the collective network can be trained in Section 2.4. We remark that, in this work, we have allowed $f(\!\cdot\!)$ to be fully flexible to take full advantage of the predictive power of neural networks. We recognise that in some applications, interpretability of the fixed effects may also be desirable. In such cases, it is possible to replace the fixed effects module (described in Section 2.3.1) by a Combined Actuarial Neural Network (CANN; Wüthrich and Merz Reference Wüthrich and Merz2019) structure.

2.3.1. Fixed Effects

The biggest limitation of GLMMs lies in their linear structure in (2.6): similar to GLMs, features need to be hand-crafted to allow for non-linearities and interactions (Richman, Reference Richman2021a,b). The proposed GLMMNet addresses this issue by utilising a multi-layer network component for the fixed effects, which is represented as the shaded structure in Figure 1. For simplicity, here we consider a fully connected feedforward neural network (FFNN), although many other network structures, for example, convolutional neural networks (CNNs), can also be easily accommodated.

A FFNN consists of multiple layers of neurons (represented by circles in the diagram) with non-linear activation functions to capture potentially complex relationships between input and output vectors; we refer to Goodfellow et al. (Reference Goodfellow, Bengio and Courville2016) for more details. Formally, for a network with L hidden layers and $q_l$ neurons in the l-th layer for $l=1,\cdots,L$ , the l-th layer is defined in Equation (2.7):

(2.7) \begin{align}\mathbf a^{(l)}\;:\; \mathbb{R}^{q_{l-1}} & \to \mathbb{R}^{q_l}, \notag \\[5pt] \mathbf v & \mapsto \mathbf a^{(l)}(\mathbf v) = \left[a_1^{(l)}(\mathbf v), \cdots, a_{q_l}^{(l)}(\mathbf v)\right]^\top, \end{align}

with

\begin{align*}a_j^{(l)}(\mathbf v) = \varphi^{(l)} \left(b_j^{(l)} + \mathbf w_j^{(l)\top} \mathbf v\right), \quad j = 1, \cdots, q_l,\end{align*}

where $\varphi^{(l)}\;:\; \mathbb{R} \to \mathbb{R}$ is called the activation function for the l-th layer, $b_j^{(l)} \in \mathbb{R}$ and $\mathbf w_j^{(l)} \in \mathbb{R}^{q_{l-1}}$ , respectively represent the bias term (or intercept in statistical terminology) and the network weights (or regression coefficients) for the j-th neuron in the l-th layer, and $q_0$ is defined as the number of (preprocessed) covariates entering the network.

The network therefore provides a mapping from the (preprocessed) covariates $\mathbf x$ to the desired output layer through a composition of hidden layers:

\begin{align*}f\;:\; \mathbb{R}^{q_0} &\to \mathbb{R}^{q_{L+1}} \notag \\[5pt] \mathbf x &\mapsto f(\mathbf x) = \left(\mathbf a^{(L+1)} \circ \cdots \circ \mathbf a^{(1)} \right) (\mathbf x).\end{align*}

Training a network involves making many specific choices; Appendix B.2 of the supplementary material gives more details.

2.3.2. Random Effects

Below we give a description of the random effects component of GLMMNet, which is the top (unshaded) structure in Figure 1. For illustration, we stay with the case of a single high-cardinality categorical feature with q unique levels. The column of the categorical feature is first one-hot transformed into a binary matrix $\textbf{Z}$ of size $n \times q$ , which forms the input to the random effects component of the GLMMNet.

Here comes the key difference from the fixed effects network: The weights in the random effects layer, denoted by $\textbf{u}=[u_1, u_2, \cdots, u_q]^\top$ , are not fixed values but are assumed to come from a distribution. This way, instead of having to estimate the effects of every individual category—many of which may lack sufficient data to support a reliable estimate, we only need to estimate the far fewer parameters that govern the distribution of $\textbf{u}$ (in our case, just a single parameter $\sigma_u^2$ in (2.8)).

A less visible yet important distinction of our GLMMNet from the LMMNN (Simchoni and Rosset, Reference Simchoni and Rosset2022) is that our model takes a Bayesian approach to the estimation of random effects (as opposed to an exact likelihood approach in the LMMNN). The Bayesian approach helps sidestep some difficult numerical approximations of the (marginal) likelihood function (as detailed in Section 2.4). We should point out that, although the Bayesian approach is popular for estimating GLMMs (e.g. Bürkner, Reference Bürkner2017), there are further computational challenges that prevent them from being applied to ML mixed models; we elaborate on this in Section 2.4.

Following the literature (McCulloch and Searle, Reference McCulloch and Searle2001; Antonio and Beirlant, Reference Antonio and Beirlant2007), we assume that the random effects follow a normal distribution with zero mean and that the random effects are independent across categories:

(2.8) \begin{align}\textbf{u} \sim \mathcal{N}(\textbf{0}, \sigma_u^2 \textbf{I}), \end{align}

or equivalently, $u_i \stackrel{iid}{\sim} \mathcal{N}(0, \sigma_u^2)$ for $i = 1, 2, \cdots, q$ . This is taken as the prior distribution of the random effects $\textbf{u}$ .

In the context of our research problem, we are interested in the predictions for $\textbf{u}$ , which indicate the deviation of individual categories from the population mean, or in other words, the excess risk they carry. Under the Bayesian framework, given the posterior distribution $p(\textbf{u} | \mathcal{D})$ , we can simply take the posterior mode $\hat{\textbf{u}} = \mathop{\textrm{argmax}}_\textbf{u} p(\textbf{u} | \mathcal{D})$ as a point estimate, which is also referred to as the maximum a posteriori (MAP) estimate. Alternatively, we can also derive interval predictions from the posterior distribution to determine whether the deviations as captured by $\textbf{u}$ carry statistical significance.

We should note that the estimation of the posterior distribution, which we treated as given in the above, represents a major challenge in Bayesian inference. We come back to this topic and discuss how we tackle it in Section 2.4.

It should also be pointed out that the extension to multiple categorical features is very straightforward: all we need is to add another random effects layer (and one more variance parameter to estimate for each additional feature).

2.3.3. Response Distribution

As explained earlier, one novel contribution of GLMMNet is its ability to accommodate many non-Gaussian distributions, notably gamma (commonly used for claim severity), (over-dispersed) Poisson (for claim counts), and Bernoulli (for binary classification tasks). Our extension to non-Gaussian distributions is an important prerequisite for insurance applications.

Precisely, we assume that the responses $Y_i, i = 1, 2, \cdots, n$ , given the random effect $u_{j[i]}$ for the category it belongs to (where j[i] means that the i-th observation belongs to the j-th category), are conditionally independent with a distribution in the ED family. Equation (2.5) implies that the conditional distribution of $Y_{i} | u_{j[i]}$ is completely specified by the conditional mean $\mu_{i}$ and the dispersion parameter $\phi$ , so we can writeFootnote 1

\begin{align*}y_i | u_{j[i]} \sim \textrm{ED}({\mu}_i, \phi).\end{align*}

In the GLMMNet, we assume that a smooth and strictly monotone link function $g(\!\cdot\!)$ connects the conditional mean $\mu_{i}$ with the non-linear predictor $\eta_{i}$ formed by adding together the output from the fixed effects module and the random effects layer:

(2.9) \begin{align}g(\mu_{i}) = \eta_{i} = f(\textbf{x}_i) + \textbf{z}_i^\top \textbf{u} = f(\textbf{x}_i) + u_{j[i]}, \;\; i = 1, \cdots, n. \end{align}

In implementation, we use the inverse link $g^{-1}(\!\cdot\!)$ as the activation function for the final layer, to map the output $[f(\textbf{x}_i), u_{j[i]}]$ from the fixed effects module and the random effects layer to a prediction for the conditional mean response $\mu_i = g^{-1}(f(\textbf{x}_i) + u_{j[i]}).$

Finally, as GLMMNet operates under probabilistic assumptions, the dispersion parameter $\phi$ (independent of covariates in our setting) can be estimated under maximum likelihood as part of the network. This is implemented by adding an input-independent but trainable weight to the final layer of GLMMNet (Chollet et al., Reference Chollet2015), whose value will be updated by (stochastic) gradient descent on the loss function.

2.4. Training of GLMMNet: Variational Inference

In a LMMNN (Simchoni and Rosset, Reference Simchoni and Rosset2022), the network is trained by minimising the negative log-likelihood of $\textbf{y}$ . As the LMMNN assumes a Gaussian density for $\textbf{y} | \textbf{u}$ and $\textbf{u} \sim \mathcal{N}(\textbf{0}, \boldsymbol{\Sigma})$ , the marginal likelihood of $\textbf{y}$ can be derived analytically, that is, $\textbf{y} \sim \mathcal{N}(f(\mathbf X), \mathbf Z \boldsymbol\Sigma \mathbf Z^\top + \sigma_\epsilon^2 \mathbf I)$ . The network weights and biases, as well as the variance and covariance parameters $\sigma_\epsilon^2$ and $\sigma_u^2$ , are learned by minimising the negative log-likelihood:

\begin{align*}-\log \mathcal{L}= \frac{1}{2}(\textbf{y}-f(\textbf{X}))^\top \textbf{V}^{-1}(\textbf{y}-f(\textbf{X})) + \frac{1}{2} \log \operatorname{det}(\textbf{V}) + \frac{n}{2} \log (2 \pi),\end{align*}

where $\textbf{V} = \mathbf Z \boldsymbol\Sigma \mathbf Z^\top + \sigma_\epsilon^2 \mathbf I$ and $\boldsymbol \Sigma := \operatorname{Cov}(\mathbf u) = \sigma_u^2 \textbf{I}$ .

In making the extension to GLMMNet, however, we no longer obtain a closed-form expression for the marginal likelihood:

(2.10) \begin{align}\mathcal{L}(\textbf{y}; \boldsymbol{\beta}, \sigma_u, \phi) &= \prod_{i=1}^n p(y_i | \boldsymbol{\beta}, \sigma_u, \phi) = {\int \prod_{i=1}^n p(y_i | \boldsymbol{\beta}, \textbf{u}, \phi) \pi(\textbf{u} | \sigma_u) \textrm{d} \textbf{u} \nonumber} \\[5pt] & = {\int \prod_{i=1}^n \prod_{j=1}^q p(y_i | \boldsymbol{\beta}, u_j, \phi) \pi(u_j | \sigma_u) \textrm{d} u_j} \end{align}

where $\boldsymbol{\beta}$ denote the weights and biases in the fixed effects component of the GLMMNet (Section 2.3.1), $\sigma_u^2$ is the variance parameter of the random effects, and $\phi$ is the usual dispersion parameter for the ED density for the response. In the work of Mandel et al. (Reference Mandel, Ghosh and Barnett2022), the authors used the Laplace method to approximate the integrand so that the resulting integral can be solved analytically. However, the Laplace method requires calculating and inverting the Hessian matrix, which can be very computationally expensive or even infeasible in high-dimensional settings (Zhang et al., Reference Zhang, Bütepage, Kjellströööm and Mandt2019).

We choose to take a Bayesian approach to circumvent the difficult numerical approximations required to compute the integral in Equation (2.10). Under the Bayesian framework, $\pi(u_j) = \pi(u_j | \sigma_u) = \mathcal{N}(0, \sigma_u^2), \;j = 1, \cdots, q$ , is taken as the prior for the random effects. Our goal is to make inference on the posterior of the random effects given by:

(2.11) \begin{align}p(\textbf{u} | \mathcal{D})=\frac{\pi(\textbf{u})p(\mathcal{D}|\textbf{u})}{p(\mathcal{D})}=\frac{\pi(\textbf{u})p(\mathcal{D}|\textbf{u})}{\int \pi(\textbf{u} )p(\mathcal{D}|\textbf{u} ) \textrm{d} \textbf{u}}, \end{align}

where $\pi(\textbf{u}) = \prod_{j=1}^q \pi(u_j)$ and $\mathcal{D}$ represents the data. Note that we again encounter an intractable integral in (2.11). The traditional solution is to use Markov chain Monte Carlo (MCMC) to sample from the posterior without computing its exact form; however, the computational burden of MCMC techniques restricts its applicability to large and complex models like a deep neural network. This partly explains why Bayesian methods, although frequently used for estimating GLMMs (e.g., Bürkner, Reference Bürkner2017), tend not to be as widely adopted among ML mixed models (e.g., Sigrist, Reference Sigrist2022; Hajjem et al., Reference Hajjem, Bellavance and Larocque2014).

With our GLMMNet, we apply variational inference to solve this problem. Variational inference is a popular approach among ML researchers working with Bayesian neural networks due to its computational efficiency (Blei et al., Reference Blei, Kucukelbir and McAuliffe2017; Zhang et al., Reference Zhang, Bütepage, Kjellströööm and Mandt2019). Variational inference does not try to directly estimate the posterior but proposes a surrogate parametric distribution $q \in \mathcal{Q}$ to approximate it, therefore turning the difficult estimation into a highly efficient optimisation problem. This contrasts with the computational burden of the Laplace approximation used in Mandel et al. (Reference Mandel, Ghosh and Barnett2022): in our experiments, we found that the runtime of the model by Mandel et al. (Reference Mandel, Ghosh and Barnett2022) can easily be on the magnitude of hours for a moderately sized dataset of less than 10,000 rows.

In practice, the choice of the surrogate distribution is often made based on simplicity or computational feasibility. One particularly popular option is to use a mean-field distribution family (Blei et al., Reference Blei, Kucukelbir and McAuliffe2017), which assumes independence among all latent variables. In this work, we mainly consider a diagonal Gaussian distribution for the surrogate posterior, which is computationally convenient (as we will see below):

\begin{align*}q_{\boldsymbol{\vartheta}}(\textbf{u})=\mathcal{N}({\boldsymbol{\mu}}_u, {\boldsymbol{\Sigma}}_u),\end{align*}

where $\boldsymbol{\mu}_u {=(\mu_1, \mu_2, \cdots, \mu_q)}^\top $ is a q-dimensional vector of the surrogate posterior mean of the q random effects, and $\boldsymbol{\Sigma}_u = \textrm{diag}(\sigma_1^2, \sigma_2^2, \cdots, \sigma_q^2)$ is a diagonal matrix of the posterior variance. More complex distributions, such as those that incorporate dependencies among latent variables, may provide a more accurate approximation. We explore this topic in Section 4.4.

The vector $\boldsymbol{\mu}_u$ and the diagonal elements of $\boldsymbol{\Sigma}_u$ together form the variational parameters of the diagonal Gaussian, denoted by $\boldsymbol{\vartheta}$ . The task here is to find the variational parameters $\boldsymbol{\vartheta}^*$ that make the approximating density $q_{\boldsymbol{\vartheta}} \in \mathcal{Q}$ closest to the true posterior, where closeness is defined in the sense of minimising the Kullback–Leibler (KL) divergence (Kullback and Leibler, Reference Kullback and Leibler1951). In mathematical terms, this means

\begin{align*}q_{\boldsymbol{\vartheta}^{*}}(\textbf{u}) &= \mathop{\textrm{argmin}}_{q_{\boldsymbol{\vartheta}}(\textbf{u}) \in \mathcal{Q}} \textrm{KL}[q_{\boldsymbol{\vartheta}}(\textbf{u}) \| p(\textbf{u} | \mathcal{D})] \\[5pt] &= \mathop{\textrm{argmin}}_{q_{\boldsymbol{\vartheta}}(\textbf{u}) \in \mathcal{Q}} \mathbb{E}_{q_{\boldsymbol{\vartheta}}(\textbf{u})} [\log q_{\boldsymbol{\vartheta}}(\textbf{u}) - \log p(\textbf{u} | \mathcal{D})] \\[5pt] &= \mathop{\textrm{argmin}}_{q_{\boldsymbol{\vartheta}}(\textbf{u}) \in \mathcal{Q}} \mathbb{E}_{q_{\boldsymbol{\vartheta}}(\textbf{u})} [\log q_{\boldsymbol{\vartheta}}(\textbf{u}) - \log \pi(\textbf{u}) - \log p(\mathcal{D} | \textbf{u})] \\[5pt] &= \mathop{\textrm{argmin}}_{q_{\boldsymbol{\vartheta}}(\textbf{u}) \in \mathcal{Q}} \left(\operatorname{KL}[q_{\boldsymbol{\vartheta}}(\textbf{u}) \| \pi(\textbf{u})]-\mathbb{E}_{q_{\boldsymbol{\vartheta}}(\textbf{u})}[\log p(\mathcal{D} | \textbf{u})]\right).\end{align*}

We take the term inside the outermost bracket, which is called the evidence lower bound (ELBO) loss or the negative of variational free energy in Neal and Hinton (Reference Neal, Hinton and Jordan1998), to be the loss function for the GLMMNet:

(2.12) \begin{align}\mathcal{L}_\textrm{GLMMNet} &= \operatorname{KL}[q_{\boldsymbol{\vartheta}}(\textbf{u}) \| \pi(\textbf{u})]-\mathbb{E}_{q_{\boldsymbol{\vartheta}}(\textbf{u})}[\log p(\mathcal{D} | \boldsymbol{\beta}, \textbf{u}, \phi)] \end{align}

(2.13) \begin{align}&= \mathbb{E}_{q_{\boldsymbol{\vartheta}}(\textbf{u})}\left[\log \frac{q_{\boldsymbol{\vartheta}}(\textbf{u})}{\pi(\textbf{u})} \right]- \mathbb{E}_{q_{\boldsymbol{\vartheta}}(\textbf{u})}[\log p(\mathcal{D} | \boldsymbol{\beta}, \textbf{u}, \phi)]. \end{align}

We see that the loss function used to optimise the GLMMNet is a sum of two parts: a first part that captures the divergence between the (surrogate) posterior and the prior, and a second part that captures the likelihood of observing the given data under the posterior. This decomposition echoes the trade-off between the prior beliefs and the data in any Bayesian analysis (Blei et al., Reference Blei, Kucukelbir and McAuliffe2017). It is also worth pointing out that there is a trade-off between the dispersion parameter $\phi$ and the random effects variance $\boldsymbol{\Sigma}_u$ which goes into $\boldsymbol{\vartheta}$ . Overly low estimates of $\phi$ can require compensatory “inflation” in the estimated $\boldsymbol{\Sigma}_u$ to account for the greater unexplained noise manifesting as increased group heterogeneity. Conversely, overestimated dispersion may prevent the random effects from being accurately estimated.

The loss function in (2.13) can be calculated via Monte Carlo, that is, by generating posterior samples under the current estimates of the variational parameters $\boldsymbol{\vartheta}$ , evaluating the respective expressions at each of the sampled values, and taking the average to approximate the expectation. Note that the mean-field Gaussian approximation is mathematically convenient as it allows for the explicit calculation of the first term in Equation (2.12); see Example 11.20 of Wüthrich and Merz (Reference Wüthrich and Merz2023, p.535):

\begin{align*} \operatorname{KL}[q_{\boldsymbol{\vartheta}}(\textbf{u}) \| \pi(\textbf{u})] &= -\frac{q}{2} + \frac{q}{2} \log \left( \sigma_u^2 \right) - \frac{1}{2}\sum_{j=1}^q \left( \log \left(\sigma^2_j \right) - \frac{\sigma_j^2 + \mu_j^2}{\sigma_u^2}\right).\end{align*}

The derivatives of the loss function —with respect to the network parameters $\boldsymbol{\beta}$ , the dispersion parameter $\phi$ , and the variational parameters $\boldsymbol{\vartheta}$ — can be computed via automatic differentiation (Chollet et al., Reference Chollet2015) and used by standard gradient descent algorithms for optimisation (e.g., Kingma and Ba, Reference Kingma and Ba2014). Appendix A of the supplementary material discusses the practicalities of implementing and training the GLMMNet.

2.5. Prediction from GLMMNet

Recall that $\textbf{x}$ denotes the standard input features and $\textbf{z} \in \mathbb{R}^q$ is a binary representation of the high-cardinality feature. Let $(\textbf{x}^*, \textbf{z}^*)$ represent a new data observation for which a prediction should be made. Note that when making predictions, it is possible to encounter a category that was never seen during training, in which case $\textbf{z}^*$ will be a zero vector (as it does not belong to any of the already-seen categories) and the predictor in Equation (2.9) reduces to $\eta^* = f(\textbf{x}^*)$ .

To make predictions from GLMMNet, we take expectations under the posterior distribution on the random effects (Blundell et al., Reference Blundell, Cornebise, Kavukcuoglu and Wierstra2015; Jospin et al., Reference Jospin, Buntine, Boussaid, Laga and Bennamoun2022):

(2.14) \begin{align}p(y^* | \textbf{x}^*, \textbf{z}^*) &= \mathbb{E}_{p(\textbf{u} | \mathcal{D})} \left[p(y^* | \textbf{x}^*, \textbf{z}^*, \textbf{u}) \right] \notag \\[5pt] & \approx \mathbb{E}_{q_{\boldsymbol{\vartheta}^{*}}(\textbf{u})} \left[p(y^* | \textbf{x}^*, \textbf{z}^*, \textbf{u}) \right] \end{align}

where $q_{\boldsymbol{\vartheta}^{*}}(\!\cdot\!)$ denotes the optimised surrogate posterior. As a result of Equation (2.14), any functional of $y^* | \textbf{x}^*, \textbf{z}^*$ , for example, the expectation, can be computed in an analogous way.

The expectation in (2.14) can be approximated by drawing Monte Carlo samples from the (surrogate) posterior, as outlined in Algorithm 1. When there are multiple high-cardinality features, under the assumption of independence between these features, each will have its own binary vector representation and optimised surrogate posterior, and the expectation in Equation (2.14) will be taken with respect to the joint posterior.

It is also worth pointing out that the model averaging in line 5 has the equivalent effect of training an ensemble of networks, which is usually otherwise computationally impractical (Blundell et al., Reference Blundell, Cornebise, Kavukcuoglu and Wierstra2015).

Algorithm 1.

Prediction from GLMMNet

3.1. Models for Comparison

Listed below are the leading models widely used in practice that allow for high-cardinality categorical features. They will be implemented and used for comparison with the GLMMNet in this experiment.

1. 1. GLM with

   • – GLM_ignore_cat: complete pooling, that is, ignoring the categorical feature altogether. In the notation of Section 2.1, it can be represented as $\mu(\textbf{x}, \textbf{z}) = g^{-1}(\beta_0 + \textbf{x}^\top \boldsymbol{\beta})$ , where $g(\!\cdot\!)$ is the link function, and $\beta_0$ and $\boldsymbol{\beta}$ are the regression parameters to be estimated;

   • – GLM_one_hot: one-hot encoding, which can be represented as $\mu(\textbf{x}, \textbf{z}) = g^{-1}(\beta_0 + \textbf{x}^\top \boldsymbol{\beta} + \textbf{z}^\top \boldsymbol{\alpha})$ where $\boldsymbol{\alpha}$ are the additional parameters for each category in $\textbf{z}$ ;

   • – GLM_GLMM_enc: GLMM encoding, which can be represented as $\mu(\textbf{x}, \textbf{z}) = g^{-1}(\beta_0 + \textbf{x}^\top \boldsymbol{\beta} + z'\alpha)$ where z’ represents the encoded value (a scalar) of $\textbf{z}$ and $\alpha$ the additional parameter associated with it. The scalar encodings are computed as the cross-validated posterior estimates of a random intercept GLMM (more details in Appendix B.1 of the supplementary material);

2. 2. Gradient boosting machine (GBM, with trees as base learners) under the same categorical encoding setup as above:

   • – GBM_ignore_cat: $\mu(\textbf{x}, \textbf{z}) = \mu(\textbf{x})$ where $\mu(\!\cdot\!)$ is a weighted sum of tree learners;

   • – GBM_one_hot: $\mu(\textbf{x}, \textbf{z})$ where $\mu(\cdot, \cdot)$ is a weighted sum of tree learners;

   • – GBM_GLMM_enc: $\mu(\textbf{x}, \textbf{z}) = \mu(\textbf{x}, z')$ where z’ represents the encoded value (a scalar) of $\textbf{z}$ ;

3. 3. Neural network with entity embeddings (NN_ee). Here, $\mu(\textbf{x}, \textbf{z})$ is composed of multiple layers of interconnected neurons, where each neuron receives inputs, performs a weighted sum of these inputs, and applies an activation function to produce its output (see also Section 2.3.1). Entity embeddings add a linear layer between each one-hot encoded input and the first hidden layer and are learned as part of the training process. This is currently the most popular approach for handling categorical inputs in deep learning models and serves as our target benchmark;

4. 4. GBM with pre-learned entity embeddings (GBM_ee), which is similar to GBM_GLMM_enc except with the z’ replaced by a vector of entity embeddings learned from NN_ee ;

5. 5. Mixed models:

   • – GLMM with $\mu(\textbf{x}, \textbf{z}) = g^{-1}(\beta_0 + \textbf{x}^\top \boldsymbol{\beta} + \textbf{z}^\top \textbf{u})$ where $\textbf{u}$ are the random effects (Section 2.2);

   • – GPBoost (Sigrist, Reference Sigrist2021, Reference Sigrist2022) with $\mu(\textbf{x}, \textbf{z}) = g^{-1}(f(\textbf{x}) + \textbf{z}^\top \textbf{u})$ where $f(\!\cdot\!)$ is an ensemble of tree learners;

   • – The proposed GLMMNet, with $\mu(\textbf{x}, \textbf{z}) = g^{-1}(f(\textbf{x}) + \textbf{z}^\top \textbf{u})$ where $f(\!\cdot\!)$ is a FFNN (Section 2.3.1).

Each model has its own features and properties, which renders the collection of models suitable for different contexts; Table 1 gives a high-level summary of their different characteristics. In the table, “interpretability” refers to the ease with which a human can understand a model, as well as the ability to derive insights from the model. For instance, GLMs have high interpretability, because GLMs have a small number of parameters and each parameter carries a physical meaning, which contrasts with the case of a standard deep neural network characterised by a large number of parameters that are meaningless on their own. “Operational complexity” refers to the resources and time required to implement, train and fine-tune the model. The last column, “bias-variance focus” gives a taste of the primary focus of each algorithm in regards to the bias variance trad-eoff; note that the descriptions can be subjective and should be interpreted in the context of the entire range of methods presented.

Table 1.

Overview of different characteristics of candidate models

3.2. Simulation Datasets

We generate n observations from a (generalised) non-linear mixed model with q categories ( $q \le n$ ). We assume that the responses $y_i, i = 1, \cdots, n$ , each belonging to a category $j[i] \in \{1, \cdots, q\}$ , given the random effects $\textbf{u}=(u_j)_{j=1}^q$ , are conditionally independent with a distribution in the ED family (e.g., gamma), denoted as $y_i | u_{j[i]} \sim \textrm{ED}({\mu}_i, \phi),$ where ${\mu}_i=\mathbb{E}(y_i | u_{j[i]})$ represents the mean and $\phi$ represents a dispersion parameter shared across all observations. A link function $g(\!\cdot\!)$ connects the conditional mean ${\mu}_i$ with a non-linear predictor in the form of

(3.1) \begin{align}g(\mu_i)=g(\mathbb{E}[{y}_i| {u}_{j[i]}])=f(\textbf{x}_i)+u_{j[i]}, \quad i = 1, \cdots, n, \end{align}

where $\textbf{x}_i$ is a vector of fixed effects covariates for the i-th observation, $\textbf{z}_i$ is a vector of random effects variables (in our case, a binary vector representation of a high-cardinality categorical feature), the random effects ${u}_{j} \stackrel{iid}{\sim} \mathcal{N}(0, \sigma_u^2)$ measure the deviation of individual categories from the population mean, and $f(\!\cdot\!)$ is some complex non-linear function of the fixed effects. Equation (3.1) also implies that there is no interaction between the high-cardinality categorical variable and the remaining covariates other than an additive relationship in the non-linear predictor. This can be a strong assumption, and we discuss it in Section 3.3.

For the fixed effects component f, we consider

(3.2) \begin{align}f(\textbf{x}) &= 10 \sin \left(\pi x_{1} x_{2}\right)+20\left(x_{3}-0.5\right)^{2}+10 x_{4}+5 x_{5}, \\[5pt] x_i & \stackrel{iid}{\sim} \mathcal{U}(0,1), \; i = 1, \cdots, 10, \notag\end{align}

which was studied in Friedman (Reference Friedman1991) and has become a standard simulation function for regression models (used in, e.g., Kuss et al. Reference Kuss, Pfingsten, Csato and Rasmussen2005; also available in the scikit-learn Python library by Pedregosa et al. Reference Pedregosa, Varoquaux, Gramfort, Michel, Thirion, Grisel, Blondel, Prettenhofer, Weiss, Dubourg, Vanderplas, Passos, Cournapeau, Brucher, Perrot and Duchesnay2011). In our application, we use the Friedman function to test the model’s ability to filter out irrelevant predictors (note that $x_6, \cdots, x_{10}$ is not used in f) as well as detect interactions and non-linearities of input features, all in the presence of a high-cardinality grouping variable that is subject to random effects.

We set up the desired simulation environments by adjusting the following parameters; refer to Appendix B.4 of the supplementary material for details.

• – Signal-to-noise ratio: a three-dimensional vector that captures the relative ratio of signal strength (as measured by $\mu_f$ , the mean of $f(\textbf{x})$ ), random effects variance ( $\sigma_u^2$ ), and variability of the response ( $\sigma_\epsilon^2$ ; noise, or equivalently the irreducible error, as this component captures the unexplained inherent randomness in the response).

• – Response distribution: distributional assumption for the response, for example, Gaussian, gamma, or any other member of the ED family.

• – Inverse link: inverse of the link function, that is, the inverse function of $g(\!\cdot\!)$ in Equation (3.1).

• – Distribution of categories: whether to use balanced or skewed distribution for the allocation of categories. A “balanced” distribution allocates approximately equal number of observations to each category; a “skewed” distribution generates categories from a (scaled) beta distribution.

These simulation parameters allow us to mimic specific characteristics of practical insurance data, such as a low signal-to-noise ratio (the specification of [8,1,4] was selected based on estimated parameters from the real insurance case study in Section 4), an imbalanced distribution across categories, or a highly skewed response distribution for claim severity. Table 2 lists the simulation environments considered in our experiment, along with a brief description below.

• – Experiment 1 simulates the base scenario that adheres to the assumptions of a Gaussian GLMMNet.

• – Experiment 2 simulates a gamma-distributed response, which is often used to model claim severity in lines of business (LoB) such as auto insurance or general liability.

• – Experiment 3 dials up the level of noise in the data and simulate LoBs that are difficult to model by covariates, such as commercial building insurance, catastrophic events, and cyber risks.

• – Experiment 4 represents the most challenging scenario, incorporating the complexities of all the simulation parameters.

Table 2.

Parameters used for the different simulation environments. Bold face indicates changes from the base scenario (i.e., experiment 1).

For each scenario, we generate 5,000 training observations and 2,500 testing observations, with $q=100$ categories.

4.1. Description of Data

The data for this analysis were provided by a major Australian insurer. The original data cover 27,351 commercial building and contents reported claims by small- and medium-sized enterprises (SME) over the period between 2010 and 2015. The analysis seeks to construct a regression model to predict the ultimate costs of the reported claims based on other individual claim characteristics that can be observed early in the claims process (e.g., policy-level details and claim causes). A description of the (post-engineered) input features is given in Appendix B.6.1 of the supplementary material.

The response variable is the claim amount (claim severity), which has a very skewed distribution, as shown in Figure 3: even on a log scale, we can still observe some degree of positive skewness. In search for a suitable distribution to model the severity, we fit both lognormal and loggamma distributions to the (unlogged) marginal. Figure 4 indicates that both models may be suitable, with loggamma being slightly more advantageous. We will test both lognormal and loggamma distributions in our experiments below (Section 4.2); specifically, we fit Gaussian and gamma models to the log-transformed response.

Figure 3.

Histogram and Gaussian kernel density estimate of claim amounts (on log scale). The x-axis numbers have been deliberately removed for confidentiality reasons.

Figure 4.

Probability–probability (P-P) plot of empirical versus fitted lognormal and loggamma (theoretical) distributions. The parametric distributions are fitted to the (unlogged) marginal, that is, without any covariates.

The high-cardinality categorical variable of interest is the occupation of the business, which is coded through the Australian and New Zealand Standard Industrial Classification (ANZSIC) system. The ANZSIC system hierarchically classifies occupations into Divisions, Subdivisions, Groups, and Classes (from broadest to finest). See Table 4 for an example.

Table 4.

An example of ANZSIC occupation classification

We will look at occupations at the Class level.Footnote 3 There are over 300 unique levels of such occupation classes in this dataset. Panel (a) of Figure 5 highlights the skewed distribution of occupations: the most common occupation has more than double the number of observations than the second most common, and the number of observations decays rapidly for the rarer classes. Panel (b) shows the heterogeneity in claims experiences between different occupation classes; calculations reveal a coefficient of variation of 160% for the occupation means. One challenge in modelling the occupation variable lies in determining how much confidence we can have in the observed claims experiences; intuitively, we should trust the estimates more when there are more data points and less when there are few data points. As explained in Section 2.2, the mixed effects models are one solution to this problem, which we will explore further in this section.

Figure 5.

Skewed distribution of occupation class and heterogeneity in experience. The axes labels have been removed to preserve confidentiality.

The dataset under consideration displays many typical characteristics of insurance data that we studied in Section 3 (see Section 3.2 in particular). For example, it is characterised by an extremely skewed response distribution (Figure 3), a low signal-to-noise ratio (there is little information that covariates can add to the marginal fits, as implied by Figure 4) and a highly disproportionate distribution of categories (Figure 5). These features of the dataset best align with those of Experiment 6 in Section 3. If the conclusions generalise, we expect that our GLMMNet will show strong performance.

4.2. Modelling Results

We consider the same candidate models as before; see Section 3.1. We also consider an $\ell_2$ -regularised GLMMNet (GLMMNet_l2) in the hope that the regularisation would further improve its performance in a high-noise environment.

We split the data into a 90% training set and a 10% test set. Where relevant, the training data are further split into an inner training set and a validation set which is used to select the optimal model hyperparameters. Table 5 presents the out-of-sample metrics for the top-performing lognormal and loggamma models, respectively, compared against a one-hot encoded GLM as a baseline (full results in Appendix C.4 of the supplementary material).Footnote 4 The category root mean squared error (“Category RMSE”) measures the RMSE of the average prediction for each category (on a log scale). In the present context, the category RMSE can be interpreted as the average accuracy of loss predictions on each sub-portfolio.

Table 5.

Comparison of lognormal and loggamma model performance (median absolute error, CRPS, negative log-likelihood, RMSE of average prediction per category) on the test (out-of-sample) set. The best values are bolded.

The metrics tell consistent stories most of the time. The results for CRPS and NLL are almost perfectly aligned, which is not surprising given that both are measures of how far the observed values depart from the probabilistic predictions. In general, we consider the CRPS and NLL measures to be more reliable estimates of the goodness-of-fit of the models than the MedAE, which only takes into account the point predictions but not the uncertainty associated with them.

The results in Table 5 indicate that the regularised GLMMNet_l2 and NN_ee are equally competitive models and surpass all other models under consideration. The GLM family of models performs poorly on this data, much worse than their mixed model counterpart, that is, GLMM, confirming that the presence of the high-cardinality categorical variable interferes with their modelling capabilities. We also remark that, while the balance property for GLMs with canonical links (Wüthrich and Buser, Reference Wüthrich and Buser2021) guarantees that the average prediction for any level of the categorical features is unbiased in the training set, this property does not seem to carry over to the testing set according to the category RMSE metrics. At the same time, the outperformance of network-based models also suggests that the dataset contains complex relationships that cannot be captured in a linear structure. One practical consideration to note is that when the environment is highly noisy and the underlying model is uncertain, such as here, the interpretability of the model becomes even more important. In such cases, models that are more interpretable are preferred, which in turn highlights the advantages of GLMMNet over NN_ee.

An important observation is that adding the $\ell_2$ regularisation term clearly helps GLMMNet navigate the data better, which suggests that the original GLMMNet overfits to the training data (as can be confirmed from its deterioration in the test performance). Indeed, the amelioration of the score proves statistically significant ( $p < 0.001$ ) under the Diebold–Mariano test (Gneiting and Katzfuss, Reference Gneiting and Katzfuss2014). The results hold for both lognormal and loggamma models.

Comparing the lognormal and loggamma models, we find that assuming a loggamma distribution for the response improves the model fit in almost all instances (except the GLM models) and across all three performance metrics. While the CRPS and NLL values are reasonably similar between the two tables, the median absolute error (MedAE) halves when moving from lognormal to loggamma. An intuitive explanation is that the lognormal models struggle more with fitting to the extreme claims and thus tend to over-predict the middle-ranged values in an attempt to boost the predictions. The loggamma models, on the other hand, are more comfortable with the extremes, as those can be captured reasonably well by the long tail. These results confirm the need to consider alternative distributions beyond the Gaussian—one major motivation for our proposed extension of the LMMNN to the GLMMNet. Even within the Gaussian framework, as demonstrated by the lognormal models presented here, the GLMMNet can still be useful.

Overall, as we have seen in the simulation experiments, the differences in model performance are relatively small due to the low signal-to-noise ratio. Only a limited number of risk factors were observed, and they are not necessarily the true drivers of the claims cost. In the experiments above, a simpler structure like GLMM sometimes performs comparably with the more complex and theoretically more capable GLMMNet. That said, the results clearly demonstrate that the GLMMNet is a promising model in terms of predictive performance. We expect to see better results with a greater volume of data, for example, when more information becomes available as a policy progresses and claims develop.

Figure 6.

Posterior predictions of (a randomly selected sample of) the random effects in 95% confidence intervals from the loggamma GLMMNet, ordered by decreasing z-scores. Occupations that do not overlap with the zero line are highlighted in vermillion (if above zero) and blue (if below zero), respectively. Occupations that do overlap with the zero line are in the shaded region. The x-axis labels have been removed to preserve confidentiality.

4.3. GLMMNet: Decomposing Random Effects Per Individual Category

One main advantage of mixed effects models in dealing with high-cardinality categorical features is the transparency they offer on the effects of individual categories. This is particularly important as it is often the case that the high-cardinality categorical feature is itself a key risk factor. For example, in this dataset, there is a lot of heterogeneity in claims experience across different occupations, but the lack of data for some makes it hard to determine how much trust can be given to the experience.

The GLMMNet can provide useful insights in this regard. Figure 6 shows the posterior predictions for the effects of individual occupations (ordered by decreasing z-scores), plotted with 95% confidence intervals. Unsurprisingly, occupations with a larger number of observations generally have tighter intervals (i.e., lower standard deviations); see Figure C.4 of Appendix C.4 in the supplementary material. This prevents us from overtrusting extreme estimates, which may happen simply due to the small sample size. In the context of technical pricing, such an analysis can be helpful for identifying occupation classes that are statistically more or less risky. This is not possible with the often equally or less high-performing neural networks with entity embeddings. With the latter, we can analyse the embedding vectors to identify occupations that are “similar” to each other in the sense of producing similar output, but we cannot go any further.

4.4. GLMMNet: Beyond Mean-Field Variational Inference

As discussed in Section 2.4, the experiments so far have been restricted to mean-field variational inference; that is, we have always assumed that the posterior estimates of the categories are independent of each other. We relax this assumption here by modelling a non-diagonal covariance matrix in the surrogate posterior. However, rather than estimating a full covariance matrix, which would involve too many parameters, we pre-cluster the categories and only include dependence between categories within the same cluster. The resulting covariance matrix is a block diagonal matrix where each block on the diagonal represents a cluster.

To form the clusters, we experimented with using the original ANZSIC Groups (one level above Class) and using k-means clustering on the textual similarity encodings of the occupations (Cerda et al., Reference Cerda, Varoquaux and Kégl2018). Table 6 reports the performance metrics of the two models under the loggamma assumption. In this example, we found that allowing for posterior dependence did not yield significant improvement in predictive performance. Inspecting the posterior covariance matrix further reveals that there is little correlation between categories in the same cluster. In such cases, it seems sufficient to stick with the simpler-to-implement mean-field Gaussian assumption for the surrogate posterior. However, as is characteristic of any modelling decision, the appropriateness of this choice largely depends on the complexity of the dataset at hand. The inclusion of posterior correlation can be particularly valuable when there is a strong suspicion of high dependence among the categories.

Table 6.

Comparison of loggamma GLMMNet performance on training and test sets.