Advanced Pricing Models (ML-based GLM/GAM Enhancements)

Purpose

Use machine learning models (e.g., GBMs, Random Forests, Neural Networks) to improve upon traditional GLMs, capturing non-linearities and interactions in pricing variables.

Primary users

Insurance actuaries and pricing analysts.

Where it fits (process/stage/trigger)

Fits in the pricing model development and enhancement stage.

Key capabilities / workflow

Analyzes pricing variables, captures non-linearities, enhances GLMs with ML models, and implements improved models.

Inputs

Historical policy and claims data, exposure data, risk attributes, external socio-economic indicators.

Outputs / Deliverables

Enhanced pricing models with improved accuracy and predictive power.

Value

Improves pricing accuracy and competitiveness by leveraging advanced ML techniques.