AI-Assisted Rating Factor Selection

Purpose

AI-Assisted Rating Factor Selection supports the identification of rating variables that are predictive and stable for insurance use cases, while helping avoid overfitting through feature selection algorithms.

Primary users

The primary user is not specified. The agent is associated with the AQS team/BU, and the owner provided is Ronan Davit.

Where it fits (process/stage/trigger)

This agent fits within the insurance rating factor selection process, where policy data, claims data, exposure variables, and risk attributes are assessed to identify variables suitable for rating analysis. The specific stage, trigger, or maturity level is not specified.

Key capabilities / workflow

The agent uses feature selection algorithms to evaluate available variables, identify those that are most predictive, check for stability, and avoid overfitting before producing selected rating variables.

Inputs

Inputs are not specified. The provided dataset information includes policy data, claims data, exposure variables, and risk attributes.

Outputs / Deliverables

Outputs are not specified. Based on the provided use case, the deliverable is the identification of predictive and stable rating variables, with overfitting avoided where possible.

Value

The value of AI-Assisted Rating Factor Selection is to support more robust insurance rating variable selection by focusing on predictive and stable factors while reducing the risk of overfitting.