GLMs offer clarity and interpretability, but they rely heavily on the quality of input variables. Traditional feature engineering often struggles to capture the subtle, high-dimensional relationships hidden in large datasets. Neural networks, on the other hand, excel at detecting such patterns.By using neural networks to pre-process and enrich input variables, insurers can feed GLMs with higher-quality, more informative features.
Beyond the Basics: How Machine Learning Can Strengthen GLMs
Generalized Linear Models (GLMs) offer clarity and interpretability, but producing them is highly time consuming and they rely heavily on the quality of input variables. Traditional feature engineering and selection of variables is done manually and may not always capture the underlying patterns hidden in large datasets. Machine learning models, on the other hand, excel at detecting such patterns. By using those models to pre-process or to squeeze the last information out the residuals, insurers can improve the way they work with GLMs.
Enhancing Traditional Actuarial Models with Modern Machine Learning
For decades generalized linear models have formed the backbone of pricing and risk assessment in insurance. Their strength lies in transparency statistical discipline and regulatory acceptance. Actuaries and regulators alike trust GLMs because assumptions are explicit, drivers are interpretable and outcomes are explainable. Yet as data volumes grow and risk patterns become more complex the limitations of purely generalized linear models are increasingly visible.
Insurers face a false dilemma. On one side stand proven actuarial models that offer control and explainability but struggle to capture the complex underlying risk patterns. On the other side are advanced machine learning techniques that promise higher predictive power but are often seen as opaque, difficult to govern and hard to justify in a regulated environment. Choosing one over the other is neither necessary nor desirable.
A Hybrid Modeling Approach
A more effective path combines the strengths of both worlds. By integrating machine learning models into the modeling process without replacing the GLM framework insurers can enhance predictive performance in less time while preserving interpretability and regulatory comfort.
The result is a hybrid modelling process that results in the familiar structure of a GLM but is enriched by insights extracted through machine learning. For instance, Gradient Boosting Machines (GBMs) can be used to model the insurance risk as first step, delivering a black box insight of insurance pricing. Existing interpretation tools on the results can than assist in selecting those features that are proven relevant to the insurance pricing and to remove those features that seemed promising to add to the data but are not selected by the GBM.
Building a GLM on top of the outcome of the GBM while using the original features will deliver a traditional premium model with all its current benefits. Statistical tests can show how well the GLM behaves in relation to the GBM fitted first. In the scientific literature, this technique is called surrogate modelling and can speed up both the tedious task of manual selection of features as well as the process of feature engineering.
In case the statistical tests show more deviation between the GBM and the GLM while one selected all the relevant features may show the existence of more complex non linear patterns and interactions within the data. This assists the actuary in deciding to look for more complex relations in only those datasets where it is relevant.
This approach delivers a fast and sound pricing process without turning the model into a black box. Complex relationships that would remain invisible in traditional modeling due to time constraints are surfaced while final pricing and risk decisions remain grounded in transparent and explainable structures.
Strengthening Predictive Power Without Losing Control
One of the main advantages of this hybrid approach is improved predictive accuracy. Machine learning models excel at detecting subtle patterns across large and heterogeneous datasets. When these insights are distilled into features that feed a GLM the model becomes more responsive to actual risk behavior without sacrificing stability.
Equally important this method fits naturally within existing actuarial processes. Insurers do not need to abandon their current modeling infrastructure or governance frameworks. Instead machine learning models act as an analytical preprocessor extension rather than a disruptive replacement. This lowers adoption risk and accelerates practical implementation.
From a regulatory perspective this structure remains robust. The final model retains clear coefficients explicit drivers and traceable impacts. New variables introduced through machine learning can be tested validated and documented using established actuarial standards.
Internal prediction beyond the GLMs
One can even go one step further in case the transparency of pricing is less relevant: next to pricing models, insurers use ‘true risk models’ to predict the insurance claims before occurrence. Features that are not allowed for pricing or not wanted by the insurers risk appetite but are relevant for the risk are usually added in those models. Predictability in those models is preferred over transparency and hence one could use alternative models.
Combined Actuarial Neural Networks (CANN) can further enhance the predictability of the GLMs used in pricing to find patterns in the data that remained invisible using the transparent pricing GLM. Building a CANN on top of the pricing GLM will result in a powerful prediction model for internal purposes.
Machine learning models as Allies Rather Than Alternatives
The key mindset shift is to stop viewing GBMs and Neural Networks as competitors to GLMs. Their value lies not in replacing trusted models but in enhancing them. GBMs will speed up the selection and engineering of features for the traditional pricing models, while Neural Networks uncover hidden value in data that traditional methods cannot easily access.
This combination improves not only technical performance but also business confidence. Underwriters gain better risk differentiation, actuaries retain control over assumptions and management benefits from more reliable pricing and portfolio steering.
What Insurers Should Do Instead
To move from theory to practice insurers should adopt a structured and pragmatic approach.
First experimentation should focus on hybrid models. GBMs can be used to generate candidate features or transformations which are then evaluated within existing GLM frameworks. This allows teams to measure added value directly and objectively.
Second every new input must be validated with full actuarial rigor. Stability over time sensitivity to data changes and explainability of impact should be tested thoroughly. Compliance with regulatory expectations should be assessed from the outset rather than treated as an afterthought.
Third capability should be built step by step. Small pilot projects focused on specific portfolios or coverage types can demonstrate tangible improvements quickly. These early successes help build trust and provide a blueprint for scaling machine learning applications across the organization.
From Advanced Modeling to Better Decisions
When implemented in this way hybrid modeling becomes more than a technical upgrade. It improves pricing accuracy, strengthens risk selection and supports more informed portfolio decisions. Most importantly it does so without breaking the trust that insurers regulators and customers place in actuarial (pricing) models.
The future of insurance modeling is not a choice between tradition and innovation. It is the intelligent combination of both. By letting machine learning enhance rather than replace GLMs insurers can unlock deeper insights from their data while maintaining control transparency and confidence in every decision.
