Hierarchical compartmental reserving models give a parametric framework to describe aggregate insurance claims processes using differential equations. The paper discusses how these models can be specified in a fully Bayesian modeling framework to jointly fit paid and outstanding claims development data; demonstrates how modelers can utilize their expertise to describe specific development features and incorporate prior knowledge into parameter estimation; explores subtle yet important differences between modeling incremental and cumulative claims payments; examines parameter variation across multiple dimensions; and introduces an approach to incorporate market cycle data into the modeling process. Examples and case studies are shown using Stan via the brms package in R.
Hierarchical Compartmental Reserving Models
Hierarchical Compartmental Reserving Models
Abstract
Year
2022
Keywords
Bayesian modeling framework, claims development data, parameter estimation, cumulative claims payments, parameter variation
Description
Hierarchical compartmental reserving models give a parametric framework to describe aggregate insurance claims processes using differential equations. The paper discusses how these models can be specified in a fully Bayesian modeling framework to jointly fit paid and outstanding claims development data; demonstrates how modelers can utilize their expertise to describe specific development features and incorporate prior knowledge into parameter estimation; explores subtle yet important differences between modeling incremental and cumulative claims payments; examines parameter variation across multiple dimensions; and introduces an approach to incorporate market cycle data into the modeling process. Examples and case studies are shown using Stan via the brms package in R.
