Three Reasons GLMs Outperform GBMs
1. Enforcing business logic.
GLMs let you easily enforce monotonicity and magnitude constraints throughout the modeling process. GBMs learn primarily from data patterns, and constraints so constraints have to be added separately, if at all. In a GBM, these constraints are much more complex to apply - either they require the GBM to be simplified to essentially replicate a GLM’s transparency or the result of the constraint may not be what a modeler or leader would expect.
2. Handling data-sparse segments.
Sometimes you know from experience that a certain category should always be surcharged, even if you don't have many exposures for it. That kind of adjustment won’t show up in your test statistics, but it will show up later through adverse selection. A GLM lets you encode expert knowledge directly into the model. A GBM, without enough data, will simply ignore it.
3. Extrapolating beyond historical data.
GLMs can project trends based on domain expertise. GBMs can only work within the bounds of their training data.
When GBMs Take the Lead
GBMs (Gradient Boosting Machines) excel at one thing: capturing complex, highly interacted patterns in large datasets.
When the data is large and complex enough that no human can reasonably specify the right functional form, GBMs thrive. They surface deep interactions a GLM would miss entirely.
The Telematics Example
Telematics is the textbook case. A telematics scoring problem can easily involve hundreds of features, with highly nonlinear relationships between claims and underlying sensor data.
Speed, acceleration, braking patterns, time of day, road type, trip duration: the interactions between these variables are too numerous for a GLM to model effectively. GBMs handle it naturally.
The Catch: Production Readiness
Predictive power aside, GBMs come with real challenges:
- Regulatory risk. Most regulators hesitate to approve pricing models that lack transparency.
- Hidden biases. GBMs can embed biases that are difficult to detect.
- Data leakage. GBMs can pick up accidental or non-causal patterns, and due to their lack of transparency it can be hard to detect.
- No actuarial adjustments. GBMs cannot be manually adjusted for low-data segments or extrapolated beyond values in the historical data used to fit the model.
There was a lot of hope created with GBMs. But just because a model has high predictive power doesn’t mean it can be implemented in a way that provides value. Many insurers continue to use them for exploratory purposes or as a part of their pricing process - but GBMs have failed to completely replace GLMs due to the weaknesses discussed so far.
GBMs remain powerful tools. For most insurers, they're just not sufficient to replace transparent pricing algorithms.
The Common Mistake: Over-Constraining GBMs
Actuaries often turn to gradient boosted machines when they expect a lot of interactions in their data. The challenge is that, once you build one, it can be hard to understand what the model is doing and how those interactions actually behave.
The instinct is to add guardrails to layer constraints on top of the model to make it explainable. But if too many constraints are needed to understand the model, it is very often better to fit a proper GLM that contains those constraints in a single fit, and end up with a model that is both better and simpler.
You start with a GBM for its power, then add so many constraints that the result is neither simple nor fully trusted. At that point, you are not improving the model, you are just building a worse GLM.
The Right Approach: Using Both
GBMs aren't the ultimate solution to every problem. They're a powerful tool that makes sense in the right context, used alongside GLMs rather than instead of them.
It comes down to four questions:
- When does transparency matter more than raw performance?
- When does complexity justify the trade-offs?
- Which technique fits the specific use case?
- Does your framework support both approaches?
Akur8's Approach: Safe GBMs in Production
For years, the gap between exploratory GBM use and safe deployment in production was too wide to bridge.
At Akur8, we've built an approach that closes this gap:
- Automatic explainability diagnostics, so actuaries can genuinely understand GBM behavior.
- Embedded governance and traceability, within the same framework built for GLMs.
- End-to-end deployment, taking any model (GLM, GBM, or both) to live rating without friction.
The result isn't GBMs made simpler. It's GBMs made usable, in a controlled, transparent, and production-ready way.
Want to go further?
Explore the Akur8 Academy section for in-depth resources, and join our upcoming webinars to hear directly from our Actuarial Data Scientists on topics like this one and more.
