Model quality assurance P&C - GenAI Explanation of Divergent Results

Purpose

Model quality assurance P&C - GenAI Explanation of Divergent Results is designed to support model quality assurance by combining local and global interpretability tools to contrast models’ decision logic and help explain divergent results in an insurance context.

Primary users

The primary user is not specified in the provided information. The agent is associated with the AQS Team/BU, and the owner is listed as Ronan Davit.

Where it fits (process/stage/trigger)

This agent fits within model quality assurance activities for insurance models, particularly when divergent results need to be reviewed, compared, or explained using interpretability information.

Key capabilities / workflow

The agent analyzes available model-related information, compares local and global interpretability outputs, checks whether results diverge, validates feature weights where relevant, and produces an explanation of the model decision logic when sufficient information is available.

Inputs

Specific user inputs are not specified. The provided dataset information includes input variables, feature weights, and output probabilities.

Outputs / Deliverables

Formal outputs are not specified. Based on the provided use case, the agent supports explanation of divergent results and comparison of models’ decision logic; any specific deliverable format should be treated as not specified.

Value

The agent helps improve understanding of model behavior in an insurance setting by supporting interpretability-driven model quality assurance and making divergent model results easier to analyze and explain.