Gini-based Model Monitoring: A General Framework with an Application to Non-life Insurance Pricing
This addresses model maintenance for insurers in dynamic environments, but it is incremental as it applies existing statistical measures to a new domain.
The study tackles concept drift in non-life insurance pricing by developing a monitoring framework using the Gini index, enabling valid inference and standardized procedures to indicate when model refitting is needed, as demonstrated on a modified real-world portfolio.
In a dynamic landscape where portfolios and environments evolve, maintaining the accuracy of pricing models is critical. To the best of our knowledge, this is the first study to systematically examine concept drift in non-life insurance pricing. We (i) provide an overview of the relevant literature and commonly used methodologies, clarify the distinction between virtual drift and concept drift, and explain their implications for long-run model performance; (ii) review and formalize common performance measures, including the Gini index and deviance loss, and articulate their interpretation; (iii) derive the asymptotic distribution of the Gini index, enabling valid inference and hypothesis testing; and (iv) present a standardized monitoring procedure that indicates when refitting is warranted. We illustrate the framework using a modified real-world portfolio with induced concept drift and discuss practical considerations and pitfalls.