A Practical Guide on Graphical Model Validation
stat.ML, cs.LG, q-fin.RM
Submitted: 2026-09-22
Updated: 2026-09-22
Code: https://github.com/dutangc/CASdatasets
License: http://creativecommons.org/licenses/by/4.0/
The gist: This manuscript formalizes the most popular model validation tools used in general insurance actuarial modeling.
Terminology
Abstract
This manuscript formalizes the most popular model validation tools used in general insurance actuarial modeling. These include graphical tools like calibration plots, actual-vs-expected plots, lift charts, Murphy diagrams, as well as classical statistical tools such as Bregman losses, deviance losses, elementary losses, Murphy's decomposition and Gini scores. Particular emphasis is placed on whether calibration and discrimination are studied under a policy-weighted or an exposure-weighted population measure. This distinction is crucial in ensuring that premium schemes are calibrated on the correct scale.
Sources
- Model Monitoring: A General Framework with an Application to Non-life Insurance Pricing
- Universal Inference for Testing Calibration of Mean Estimates within the Exponential Dispersion Family
- Model Comparison and Calibration Assessment: User Guide for Consistent Scoring Functions in Machine Learning and Actuarial Practice
- Assessing model calibration with boosting trees
- The Murphy Decomposition and the Calibration-Resolution Principle: A New Perspective on Forecast Evaluation
- The Balance Property: The Constrained Case, with a View on Risk Sharing
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