AI-Based Exposure Normalization

Purpose

AI-Based Exposure Normalization is designed to adjust and normalize exposure data using machine learning, including correcting inconsistent vehicle classifications or property attributes in insurance-related datasets.

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 where insurance exposure data needs to be prepared, corrected, or standardized before further use. The provided context mentions exposure data, geospatial data, and underwriting inputs as relevant datasets.

Key capabilities / workflow

The agent analyzes exposure data, identifies whether the data is usable, flags missing or unclear details when needed, applies normalization to inconsistent attributes, checks whether inconsistencies remain, and iterates until normalized data can be delivered.

Inputs

Typical inputs are not specified as a formal input field. The provided datasets are exposure data, geospatial data, and underwriting inputs.

Outputs / Deliverables

The outputs are not specified as a formal output field. Based on the provided use case, the deliverable is adjusted and normalized exposure data, including corrected vehicle classifications or property attributes where applicable.

Value

The agent supports more consistent and usable insurance exposure data by applying machine learning to normalize inconsistent classifications or attributes, helping improve the quality of data used in downstream insurance processes.