Reinsurance P&C - Exposure Data Quality Validation

Purpose

Reinsurance P&C - Exposure Data Quality Validation supports AI-driven anomaly detection for property and casualty exposure data, focusing on issues such as incorrect geocodes, limits, or occupancy types.

Primary users

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

Where it fits (process/stage/trigger)

This agent fits into exposure data quality validation processes for insurance and reinsurance contexts, particularly when policy-level exposure data needs to be checked against geospatial datasets and historical correction logs.

Key capabilities / workflow

The workflow analyzes policy-level exposure data, compares it with available geospatial datasets and historical correction logs, detects potential anomalies, and routes records for additional review when quality issues are identified.

Inputs

The provided inputs are policy-level exposure data, geospatial datasets, and historical correction logs. No additional user-entered inputs were specified.

Outputs / Deliverables

The explicit outputs were not specified in the provided information. Based on the stated use case, the deliverable is related to anomaly detection for exposure data quality validation, with any final reporting format not specified.

Value

The value of the agent is to help identify exposure data quality issues in insurance datasets, supporting better validation of fields such as geocodes, limits, and occupancy types before the data is used downstream.