Predictive Modeling in Insurance: Beyond GLMs

Limitations of GLMs

While generalized linear models remain the industry standard for insurance pricing, they have well-known limitations. GLMs assume a specific link function and distribution family, require the actuary to pre-specify interactions and non-linear effects, and may miss complex patterns in high-dimensional data. Tree-based methods (decision trees, random forests, gradient boosted machines) and neural networks can capture non-linear relationships and high-order interactions automatically, potentially improving predictive accuracy. These methods have proven particularly effective for identifying risk segments that GLMs miss.

Practical Integration

Most insurers do not replace GLMs entirely with machine learning models. Instead, they use a complementary approach: machine learning models identify important features, interactions, and non-linear effects, which are then incorporated into the GLM framework for regulatory filings. This approach combines the predictive power of machine learning with the interpretability and regulatory acceptance of GLMs. Gradient boosted machines (GBMs) are the most popular alternative to GLMs in insurance pricing due to their strong performance, reasonable interpretability through partial dependence plots, and efficient handling of missing values and categorical variables. Model validation, fairness testing, and regulatory compliance remain critical regardless of the modeling technique used.