Individual Claims Reserving using Activation Patterns

TL;DR

This paper introduces a novel individual claims reserving model based on activation patterns of multiple coverages, improving reserve prediction accuracy by modeling coverage activation and development over time.

Contribution

The paper presents a new multinomial logistic regression-based model that captures coverage activation dynamics for individual claims reserving, enhancing prediction accuracy.

Findings

Accurate total reserve predictions demonstrated on Canadian automobile data.

Model provides detailed insights into coverage-specific reserve development.

Improved reserve estimation over traditional methods.

Abstract

The occurrence of a claim often impacts not one but multiple insurance coverages provided in the contract. To account for this multivariate feature, we propose a new individual claims reserving model built around the activation of the different coverages to predict the reserve amounts. Using the framework of multinomial logistic regression, we model the activation of the different insurance coverages for each claim and their development in the following years, i.e. the activation of other coverages in the later years and all the possible payments that might result from them. As such, the model allows us to complete the individual development of the open claims in the portfolio. Using a recent automobile dataset from a major Canadian insurance company, we demonstrate that this approach generates accurate predictions of the total reserves as well as of the reserves per insurance coverage. This allows the insurer to get better insights in the dynamics of his claims reserves.