Data Drift Monitoring for Model Inputs

Purpose

Data Drift Monitoring for Model Inputs is designed to continuously monitor changes in data distributions between training datasets and current datasets in order to detect drift in model inputs.

Primary users

The primary user is not specified in the provided information.

Where it fits (process/stage/trigger)

This agent fits into model monitoring activities after models are trained and when current temporal data samples become available for comparison against training data.

Key capabilities / workflow

The agent analyzes model input datasets, compares current data distributions with training data distributions, checks whether a training baseline is available, detects whether drift is present, and produces monitoring output based on the observed differences.

Inputs

Typical inputs include model input datasets for pricing, reserving, and exposure, as well as temporal data samples. Additional input details are not specified.

Outputs / Deliverables

Outputs are not specified in the provided information; the expected deliverables relate to drift detection findings between training and current datasets.

Value

The agent helps insurance teams identify changes in model input data over time, supporting better awareness of potential model performance risks caused by shifts in data distributions.