Model quality assurance P&C - Explainability & Transparency Generator

Purpose

The purpose of Model quality assurance P&C - Explainability & Transparency Generator is to support model quality assurance in insurance by detecting bias in predictive outcomes across demographic or geographic subgroups.

Primary users

The primary user is not specified in the provided information. The agent is associated with the AQS team and owned by Ronan Davit.

Where it fits (process/stage/trigger)

This agent fits within a model quality assurance process for P&C insurance predictive models, particularly when model predictions need to be reviewed for bias across demographic or geographic subgroups.

Key capabilities / workflow

The agent uses policyholder attributes, claims history, and model predictions to analyze predictive outcomes by subgroup, check whether bias is detected, and support explainability and transparency needs as indicated by the agent name.

Inputs

The provided dataset inputs are policyholder attributes, claims history, and model predictions. No other input requirements were specified.

Outputs / Deliverables

The formal outputs are not specified in the provided information. Please refer to documentation for the exact deliverables produced by the agent.

Value

The value of this agent is to help insurance teams identify potential bias in predictive model outcomes and support model quality assurance with an explainability and transparency-oriented review.