Model quality assurance P&C - Bias and Fairness Testing Assistant

Purpose

Model quality assurance P&C - Bias and Fairness Testing Assistant is designed to support quality assurance activities for insurance models by using AI algorithms to test the sensitivity of outputs to key assumptions or input perturbations.

Primary users

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

Where it fits (process/stage/trigger)

This agent fits within model quality assurance activities for Property & Casualty insurance, particularly when model outputs need to be tested against changes in assumptions, input data, or model parameters.

Key capabilities / workflow

The agent collects input data, model parameters, and output simulations, checks whether the available information is complete, analyzes perturbations, evaluates potential bias or fairness concerns, and supports validation of simulation results before delivering quality assurance findings.

Inputs

Typical inputs include input data, model parameters, and output simulations. No additional input requirements were specified.

Outputs / Deliverables

Outputs are not specified in the provided information. Expected deliverables should be treated as not specified and should refer to documentation or manual configuration where needed.

Value

The agent helps improve confidence in insurance model quality by supporting sensitivity testing against key assumptions and input perturbations, helping teams identify where model outputs may be affected by bias, fairness issues, or parameter changes.