The debate between Generalised Linear Models (GLMs) and Gradient Boosting Machines (GBMs) has been a long-running fixture in actuarial pricing discussions. While it is often framed as a competition between “old” and “new” modelling approaches, practitioners increasingly recognise that the real issue is not superiority, but suitability.
According to Thomas Holmes, chief actuarial officer at Akur8, both models have clear strengths — but those strengths apply in very different contexts. The decision is less about which performs better in theory, and more about which delivers the right balance of control, explainability, and predictive power in practice.
Why GLMs remain the backbone of actuarial pricing
GLMs continue to dominate core insurance pricing for one fundamental reason: they are interpretable and controllable. Each variable within a GLM can be individually understood, adjusted, and validated by an actuary. This makes it possible to explain precisely how a rating factor contributes to a final premium, something that remains essential in regulated insurance environments.
In practical terms, GLMs allow actuaries to embed domain expertise directly into the model structure. Whether it is enforcing smooth trends across age bands, adjusting discounts or surcharges, or ensuring logical extrapolation beyond observed data, GLMs provide a level of manual control that GBMs cannot easily replicate.
Three structural advantages stand out. First is the ability to enforce business logic. GLMs allow for clear constraints such as monotonic relationships, ensuring that pricing behaves in a predictable and defensible way.
GBMs, by contrast, learn patterns directly from data, meaning constraints must be layered on separately and are often less intuitive or harder to guarantee.
Second is their strength in data-sparse environments. In insurance portfolios where certain categories have limited exposure, GLMs allow actuaries to apply expert judgement rather than relying purely on observed frequency. This reduces the risk of underpricing segments that may appear statistically insignificant but carry meaningful risk.
Third is extrapolation. GLMs can extend trends beyond observed data using structured assumptions, whereas GBMs are inherently bound to the data they are trained on.
Where GBMs outperform traditional modelling approaches
GBMs, however, excel in a very different environment. Their primary advantage lies in their ability to capture highly complex, non-linear interactions across large datasets without requiring explicit model specification from the actuary.
This makes them particularly valuable in data-rich, high-dimensional problems where relationships between variables are too intricate to define manually.
Telematics is a clear example. Modern driving datasets include hundreds of behavioural signals — speed, braking intensity, acceleration patterns, trip timing, and road type — all interacting in ways that are difficult to predefine. GBMs can naturally absorb these interactions and uncover patterns that a GLM would struggle to represent without significant feature engineering.
In such contexts, GBMs often deliver superior predictive performance. However, this comes with trade-offs that limit their standalone use in production pricing environments.
The production challenge: performance vs transparency
Despite their modelling power, GBMs introduce significant challenges when moved from experimentation to deployment.
Regulatory scrutiny is a major factor. Many regulators remain cautious about pricing models that cannot be fully explained or audited. This lack of transparency can create barriers to approval, particularly in personal lines insurance.
There is also the issue of hidden bias. Because GBMs operate as complex ensembles of decision trees, they can inadvertently learn patterns that are not causal or are difficult to detect without deep model interrogation.
Data leakage is another concern. GBMs may pick up on proxy variables or spurious correlations that improve performance in testing but fail under real-world conditions.
Crucially, GBMs offer limited scope for manual adjustment. Unlike GLMs, they do not easily allow actuaries to intervene in low-data segments or impose structured corrections based on domain knowledge. This reduces flexibility in production environments where judgement is often required.
Forcing GBMs to behave like GLMs
A frequent mistake among practitioners is attempting to make GBMs more interpretable by layering constraints and adjustments on top of them. While this can improve governance, it often undermines the very advantage GBMs are chosen for in the first place.
As Holmes and his team note, excessive constraint layering can lead to a model that is neither fully explainable nor fully performant. At that point, the model effectively becomes a more complicated and less efficient version of a GLM.
This creates a paradox: starting with a GBM for flexibility, only to gradually rebuild GLM-like structure through manual restrictions.
A dual-model future for actuarial pricing
Rather than treating GLMs and GBMs as competing methodologies, the more effective approach is to view them as complementary tools.
GLMs remain essential where transparency, regulatory compliance, and expert adjustment are required. GBMs are most valuable in exploratory analysis and high-dimensional prediction tasks where interaction effects dominate.
The key decision framework can be summarised around four questions: the importance of transparency, the level of acceptable complexity, the suitability of each method for the specific use case, and whether organisational tooling supports both approaches effectively.
Akur8’s approach reflects this hybrid philosophy. By embedding explainability diagnostics, governance layers, and deployment infrastructure into a unified system, the firm aims to make both GLMs and GBMs usable in production environments. The goal is not to simplify GBMs into GLMs, but to ensure both can coexist safely within regulated pricing workflows.
Ultimately, the GLM versus GBM debate is less about choosing a winner and more about understanding context. In modern actuarial science, the most effective pricing strategies are those that know when to prioritise clarity — and when to embrace complexity.
Read the full blog from Akur8 here.
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