A surreal illustration of neural network combined with actuarial data, showcasing GLMMNet's integration of technology and risk analysis.

Unlock Actuarial Insights: How Machine Learning Tames High-Cardinality Categorical Features

Elliot Brynn in Tech & Innovation December 2025 • 4 min read.

"Discover the GLMMNet approach and how it's revolutionizing risk assessment in insurance and beyond."

Machine learning (ML) is reshaping numerous fields, including actuarial science. Yet, the unique challenges posed by insurance data often require more than general-purpose ML algorithms can offer. One significant hurdle is the prevalence of high-cardinality categorical features—variables with a large number of categories—which conventional methods struggle to process effectively. Think of occupation in commercial property insurance or the cause of injury in workers' compensation claims; these factors significantly influence risk but are difficult for traditional ML models to interpret.

Traditional methods like one-hot encoding falter when faced with high-cardinality features. One-hot encoding, which converts each category into a binary attribute, becomes computationally expensive and leads to data sparsity as the number of categories grows. This approach also assumes independence between categories, an assumption that often doesn't hold in real-world scenarios.

Enter the Generalised Linear Mixed Model Neural Network, or GLMMNet, a novel approach designed to model high-cardinality categorical features effectively. GLMMNet integrates a generalised linear mixed model within a deep learning framework. This offers the predictive power of neural networks alongside the transparency of random effects estimates, a benefit often lost with entity embedding models.

What is GLMMNet and How Does It Work?

A surreal illustration of neural network combined with actuarial data, showcasing GLMMNet's integration of technology and risk analysis.

GLMMNet fuses deep neural networks with generalised linear mixed models (GLMMs). Neural networks excel at capturing complex, non-linear relationships in data, while GLMMs provide a transparent, interpretable structure, especially for categorical variables. This combination offers a powerful tool for actuaries and other professionals dealing with complex datasets.

At its core, GLMMNet extends the Linear Mixed Model Neural Network (LMMNN) by accommodating a wider range of data distributions. While LMMNN is limited to Gaussian distributions, GLMMNet handles the exponential dispersion (ED) family, making it suitable for modeling claim frequency, severity, and pure risk premiums across various insurance contexts.

Neural Network Component: GLMMNet uses a multi-layer neural network to learn complex relationships among standard features.
GLMM Component: High-cardinality categorical features are modeled as random effects within a GLMM structure, providing transparency and interpretability.
Variational Inference: GLMMNet uses variational inference to estimate model parameters, balancing accuracy and computational efficiency.

The architecture balances predictive accuracy with interpretability. The neural network captures non-linear relationships, while the GLMM component offers insights into the impact of different categories within the high-cardinality feature. This transparency is crucial in actuarial applications, where understanding risk factors is as important as predicting outcomes.

The Future of Actuarial Modeling

GLMMNet offers a promising path forward in actuarial modeling, providing a blend of predictive power, transparency, and flexibility that traditional methods lack. As machine learning continues to evolve, models like GLMMNet will empower actuaries to gain deeper insights into risk and make more informed decisions. While there is no 'one size fits all' approach for each situation, models such as GLMMNet are a welcome addition to the actuary's toolbox, and offers flexibility to accommodate a wide range of real world scenarios.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1017/asb.2024.7,

Title: Machine Learning With High-Cardinality Categorical Features In Actuarial Applications

Subject: stat.ml cs.lg econ.em q-fin.rm

Authors: Benjamin Avanzi, Greg Taylor, Melantha Wang, Bernard Wong

Published: 30-01-2023

Everything You Need To Know

What is GLMMNet, and how does it improve on traditional methods for actuarial modeling?

GLMMNet, or the Generalised Linear Mixed Model Neural Network, is a novel approach in actuarial science that combines deep learning with generalised linear mixed models (GLMMs). It tackles the challenge of high-cardinality categorical features, which traditional methods like one-hot encoding struggle with. One-hot encoding becomes computationally expensive and leads to data sparsity as the number of categories grows. GLMMNet integrates a neural network for complex relationship capture and a GLMM structure for transparency and interpretability, especially for categorical variables. This combination allows for improved predictive accuracy while maintaining the ability to understand the impact of different risk factors, a key advantage in actuarial applications.

How does GLMMNet handle high-cardinality categorical features, and why is this important in insurance risk assessment?

GLMMNet models high-cardinality categorical features as random effects within a GLMM structure. This is critical in insurance risk assessment because many important variables, such as occupation or cause of injury, have a large number of categories. Traditional methods like one-hot encoding become inefficient with these features. By using GLMMs, GLMMNet provides a transparent and interpretable structure, allowing actuaries to understand how each category within a high-cardinality feature influences risk. This level of understanding is essential for accurate risk assessment and pricing.

What are the core components of GLMMNet, and how do they contribute to its functionality?

GLMMNet comprises three core components: a neural network, a GLMM component, and variational inference. The neural network captures complex, non-linear relationships among standard features. The GLMM component models high-cardinality categorical features as random effects, offering transparency. Finally, variational inference is used to estimate model parameters, balancing accuracy with computational efficiency. Together, these components allow GLMMNet to predict outcomes accurately while providing insights into the influence of different risk factors.

How does GLMMNet differ from the Linear Mixed Model Neural Network (LMMNN), and what implications does this have for its application?

GLMMNet extends the Linear Mixed Model Neural Network (LMMNN) by accommodating a wider range of data distributions. LMMNN is limited to Gaussian distributions, whereas GLMMNet handles the exponential dispersion (ED) family, making it suitable for modeling claim frequency, severity, and pure risk premiums across various insurance contexts. This flexibility means that GLMMNet can be applied to a broader range of insurance problems, offering a more versatile tool for actuaries dealing with different types of data and risk scenarios.

What are the benefits of using GLMMNet over traditional machine learning or statistical methods in actuarial science?

GLMMNet offers a blend of benefits that traditional methods lack. It provides enhanced predictive accuracy through its neural network component, while the GLMM component offers transparency in how high-cardinality categorical features influence outcomes. This allows for a deeper understanding of risk factors. The model's ability to handle a wider range of data distributions also contributes to its flexibility. Overall, GLMMNet empowers actuaries to gain deeper insights into risk, make more informed decisions, and move beyond the limitations of earlier methods such as one-hot encoding.