How data transformations improve model performance and define risk constraints
Raw data rarely arrives in a form that statistical models can use effectively. Feature engineering transforms raw observations into signals that expose the underlying patterns we care about—risk drivers, behavior shifts, and predictive relationships that directly inform pricing decisions.
In insurance pricing, feature engineering serves two critical purposes: it improves predictive accuracy by capturing non-linear relationships and interactions, and it encodes domain expertise by translating actuarial intuition into model constraints that regulators and stakeholders can understand and trust.
Key insight: The best feature engineering doesn't just improve metrics—it makes models interpretable, aligns them with business logic, and ensures they're robust enough to pass regulatory scrutiny.
Binning converts continuous variables into discrete categories, which helps models capture non-linear relationships while maintaining interpretability. In insurance, this is essential for features like driver age, vehicle age, or credit score.
Why it works: Binning allows the model to learn that risk doesn't increase linearly with age. Young drivers and elderly drivers both carry higher risk, but for different reasons. The model can assign appropriate coefficients to each bin without forcing a monotonic relationship.
Capping limits extreme values to prevent outliers from distorting model coefficients. This is particularly important for features like vehicle value, mileage, or claim history where a small number of extreme cases could skew predictions for the entire portfolio.
Why it works: Without capping, a few ultra-expensive vehicles could force the model to overweight the vehicle value coefficient, leading to poor predictions for the bulk of the portfolio. Capping ensures the model learns robust patterns that generalize well.
When categorical features have many levels, grouping similar categories reduces noise and improves statistical power. This is common for features like vehicle make/model, occupation codes, or geographic territories.
Why it works: Grouping reduces the degrees of freedom in the model, which improves stability and reduces overfitting. It also encodes domain knowledge—luxury brands tend to have similar risk profiles regardless of the specific manufacturer.
Mathematical transformations can linearize non-linear relationships, stabilize variance, and make distributions more suitable for modeling. This is especially useful for highly skewed features like claim amounts or exposure measures.
Why it works: Insurance data often has long-tailed distributions. Transformations compress the tail, making the data more suitable for linear models while preserving the relative ordering of observations.
Feature engineering isn't just about improving accuracy—it's about encoding business logic and actuarial principles that make models trustworthy and explainable. These constraints ensure that model predictions align with domain expertise and regulatory expectations.
Actuarial principle: "The more expensive the car, the bigger the risk and the higher the loss cost for collision coverage."
Actuarial principle: "Longer tenure with the company indicates lower risk (loyalty, responsible behavior)."
Actuarial principle: "Higher mileage increases exposure and accident probability."
Well-designed feature engineering delivers measurable improvements across multiple dimensions: predictive accuracy, model stability, regulatory acceptance, and operational maintainability.
Key Takeaway:
Feature engineering is where data science meets domain expertise. The best transformations don't just improve model metrics—they encode actuarial principles, ensure regulatory compliance, and create models that stakeholders can trust and understand. In insurance pricing, this alignment between statistical performance and business logic is not optional; it's the foundation of sustainable, scalable pricing systems.