Connecting actuarial judgment to probabilistic learning techniques with graph theory

TL;DR

This paper explores the use of graphical models in actuarial science, demonstrating their ability to represent complex dependencies and incorporate actuarial judgment, with applications to insurance claims and dynamic data.

Contribution

It introduces a framework for applying graphical models to actuarial data, highlighting their advantages and potential for probabilistic learning in insurance contexts.

Findings

Graphical models effectively represent complex dependencies in insurance data.

The approach allows integration of qualitative actuarial judgment.

Potential for dynamic data analysis with telematics is demonstrated.

Abstract

Graphical models have been widely used in applications ranging from medical expert systems to natural language processing. Their popularity partly arises since they are intuitive representations of complex inter-dependencies among variables with efficient algorithms for performing computationally intensive inference in high-dimensional models. It is argued that the formalism is very useful for applications in the modelling of non-life insurance claims data. It is also shown that actuarial models in current practice can be expressed graphically to exploit the advantages of the approach. More general models are proposed within the framework to demonstrate the potential use of graphical models for probabilistic learning with telematics and other dynamic actuarial data. The discussion also demonstrates throughout that the intuitive nature of the models allows the inclusion of qualitative knowledge or actuarial judgment in analyses.