Feature Engineering for Actuarial Predictive Models

What Is Feature Engineering?

Feature engineering is the process of creating, transforming, or selecting input variables (features) to improve model performance. In actuarial modeling, raw data often needs transformation before it can be effectively used. Good features can dramatically improve prediction accuracy, sometimes more than the choice of algorithm. Feature engineering requires domain knowledge about insurance, risk factors, and policyholder behavior.

Common Transformations

Log transformation: applied to right-skewed variables like claim amounts or income. It stabilizes variance and makes relationships more linear. Square root and Box-Cox transformations serve similar purposes. Polynomial features: adding X^2 or X*Y (interaction terms) captures non-linear relationships in linear models. Binning: converting continuous variables into categories (e.g., age groups) can capture non-monotonic effects and improve robustness.

Date-based features: extract year, month, day of week, time since an event, or seasonal indicators from date fields. In insurance, policy duration, time since last claim, and seasonality of claims are important features.

Domain-Specific Features

Actuarial feature engineering leverages insurance knowledge. Examples: loss ratio (losses / premium), claim frequency (claims / exposure), loss development factors, territory-based variables, and relativities from existing rating plans. Ratios and interaction terms often capture important relationships that raw variables miss. For Exam SRM, understand that feature engineering is an iterative process: create features, evaluate their impact on model performance (using cross-validation), and refine. The quality of features depends heavily on understanding the underlying business problem.