Futuristic cityscape representing AI-powered risk assessment in actuarial science.

Decoding Actuarial Science: How AI Learns to Predict the Future of Risk

Jordan Keane in Tech & Innovation December 2025 • 4 min read.

"Explore the groundbreaking role of machine learning and high-cardinality categorical features in revolutionizing actuarial applications, offering new levels of precision and insight."

In an era defined by data, the actuarial profession—focused on assessing and managing risk—finds itself at a pivotal intersection with machine learning (ML). For decades, actuaries have relied on statistical models to forecast future events, from insurance claims to investment returns. However, the rise of ML, particularly its ability to handle complex datasets, has opened new frontiers in risk analysis, promising greater accuracy and deeper insights.

Actuarial data often presents unique challenges, one of the most significant being the presence of high-cardinality categorical features. These are variables with a large number of categories or levels, such as occupation in commercial property insurance or specific causes of injury in workers' compensation. Traditional methods struggle to effectively process such features, leading to potentially incomplete or biased risk assessments.

This article delves into how ML techniques are being adapted and refined to overcome the limitations of conventional actuarial modeling. We'll explore a novel approach, the Generalised Linear Mixed Model Neural Network (GLMMNet), designed to harness the power of high-cardinality categorical features, offering both enhanced predictive capabilities and increased transparency—a crucial element for trust and interpretability in actuarial applications.

The Challenge of High-Cardinality Categorical Features

Futuristic cityscape representing AI-powered risk assessment in actuarial science.

Traditional methods, such as one-hot encoding, which converts categorical variables into binary representations, quickly become unwieldy and inefficient as the number of categories increases. The orthogonality (independence) assumption of one-hot encoding becomes less valid, as numerous categories inevitably start interacting. Computationally, the resulting high-dimensional feature matrix poses challenges, especially when used with complex models like neural networks. Furthermore, the uneven distribution of data across categories makes it difficult to accurately model the behavior of rare categories.

Consider the example of workers' compensation claims: The 'cause of injury' variable might have hundreds of unique categories, ranging from common incidents like lifting to rarer events like 'crash of a rail vehicle'. With one-hot encoding, each cause becomes a separate attribute, leading to thousands of parameters to estimate. Moreover, many of these causes will have very few associated claims, making it difficult to develop reliable estimates of their impact on claim severity.

Manual Regrouping: Grouping categories requires significant domain expertise and can discard valuable granular information.
Entity Embeddings: While powerful, these methods often lack transparency, making it difficult to understand how the embeddings affect the response.
Generalized Linear Mixed Models (GLMMs): Though interpretable, GLMMs inherit limitations from GLMs, prompting exploration of ML alternatives.

The GLMMNet emerges as a compelling solution, integrating a generalised linear mixed model within a deep learning framework. This approach offers the predictive power of neural networks while retaining the transparency of random effects estimates, a feature not found in entity embedding models. Its flexibility across the exponential dispersion family makes it broadly applicable in various actuarial contexts.

A New Era of Actuarial Modeling

The GLMMNet represents a significant step forward in actuarial modeling, offering a powerful and transparent approach to handling high-cardinality categorical features. By combining the strengths of deep learning with traditional statistical methods, this innovation paves the way for more accurate, reliable, and interpretable risk assessments. This fusion not only enhances predictive performance but also fosters greater confidence and understanding in the models that drive critical decisions in the insurance industry and beyond.

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Everything You Need To Know

Why is machine learning becoming so important in actuarial science?

Machine learning offers powerful tools for analyzing complex datasets, providing potentially greater accuracy and deeper insights in risk analysis compared to traditional statistical methods. This is especially crucial given the increasing volume and complexity of data in the actuarial field, allowing for more precise predictions of future events such as insurance claims and investment returns. As machine learning continues to evolve, its role in actuarial science is expected to expand, leading to more data-driven and sophisticated risk management strategies.

What are high-cardinality categorical features and why are they challenging for traditional actuarial models?

High-cardinality categorical features are variables with a large number of distinct categories or levels, like specific occupations in insurance or causes of injury in workers' compensation. Traditional actuarial models, like those using one-hot encoding, struggle with these features because the resulting high-dimensional feature matrix becomes computationally unwieldy, the independence assumption becomes less valid, and data is often unevenly distributed across categories, making it difficult to develop reliable estimates, especially for rare categories. For example, trying to model 'cause of injury' in workers' compensation where hundreds of unique causes exist creates a very sparse and high-dimensional problem.

Can you explain the Generalised Linear Mixed Model Neural Network (GLMMNet) and how it addresses the limitations of traditional methods in actuarial modeling?

The GLMMNet is a novel approach that integrates a generalised linear mixed model within a deep learning framework. This combines the predictive power of neural networks with the transparency of random effects estimates, a feature not typically found in entity embedding models. By leveraging both techniques, GLMMNet can handle high-cardinality categorical features more effectively than traditional methods. The GLMMNet's flexibility across the exponential dispersion family also allows it to be applied in a variety of actuarial contexts, and can capture complex relationships in the data while retaining interpretability.

What are the shortcomings of using manual regrouping and entity embeddings when dealing with high-cardinality categorical features in actuarial modeling?

Manual regrouping, while simple, requires significant domain expertise and may result in the loss of valuable granular information. This can lead to less accurate or biased risk assessments. Entity embeddings, although powerful, often lack transparency, making it difficult to understand how the embeddings affect the response variable. This lack of interpretability can hinder trust and confidence in the model, particularly in actuarial applications where understanding the drivers of risk is critical for decision-making.

How does the GLMMNet contribute to increased transparency in actuarial modeling, and why is this transparency important?

The GLMMNet retains the transparency of random effects estimates, which is a feature not found in entity embedding models. This means that actuaries can better understand how different categories within a high-cardinality feature are influencing predictions. Transparency is vital in actuarial modeling because it fosters trust and confidence in the models that drive critical decisions in the insurance industry and beyond. It allows stakeholders to understand the rationale behind risk assessments and ensure that the models are fair, reliable, and aligned with business objectives.