AI-Based Outlier Detection in Model Outputs

Purpose

Identify unusual results or volatility in model outputs due to poor data quality.

Primary users

Actuaries and data scientists in the insurance industry.

Where it fits (process/stage/trigger)

Fits in the model validation and quality assurance stages.

Key capabilities / workflow

Analyzes model output datasets to detect outliers, identifies causes of volatility, and generates detailed reports.

Inputs

Model output datasets (loss ratios, reserves, premiums), benchmark data.

Outputs / Deliverables

Outlier detection reports with identified causes of volatility.

Value

Improves model reliability by identifying and addressing data quality issues.