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

Purpose

Advanced Pricing Models (ML-based GLM/GAM Enhancements) is designed to improve traditional GLM-based pricing approaches by using machine learning models such as GBMs, Random Forests, and Neural Networks to capture non-linearities and interactions in pricing variables.

Primary users

The primary user is not specified in the provided information. The agent is associated with the AQS team and is intended for use in an insurance pricing context.

Where it fits (process/stage/trigger)

This agent fits within the insurance pricing model development or enhancement process, particularly when historical policy and claims data, exposure data, risk attributes, and external socio-economic indicators are available for analysis.

Key capabilities / workflow

The workflow analyzes the available pricing-related datasets, checks whether the data is sufficient, trains machine learning-based enhancements, evaluates whether the resulting approach improves GLM or GAM pricing models, and iterates until the model improvement criteria are met.

Inputs

Inputs are not specified in the provided information. The available dataset information includes historical policy and claims data, exposure data, risk attributes, and external socio-economic indicators.

Outputs / Deliverables

Outputs are not specified in the provided information. Please refer to documentation or the owner for the exact deliverables produced by the agent.

Value

The agent supports improved insurance pricing by helping capture non-linear relationships and variable interactions that may not be fully represented in traditional GLM-based pricing models.