Dynamic factor models for claim reserving

Abstract

This study presents a new approach to claim reserving in the insurance industry using dynamic factor models (DFMs). Traditional methods often struggle to adapt to temporal variations in loss development, a gap that DFMs can effectively address. By employing DFMs on a multivariate time series of loss development factors (LDFs), we offer a more sensitive adaptation and understanding of loss development over time. Our methodology not only facilitates adjustment to trends in loss development but also provides clear explanations for the underlying reasons behind these trends. This aspect is crucial for actuaries, whose responsibilities include offering transparent and understandable reserve estimates. We apply the proposed DFMs to datasets from two different lines of business, demonstrating their ability to capture the temporal evolution of factors influencing loss development. The results indicate that our approach enhances fitting ability and provides deep insights into the dynamics of claim reserving. Furthermore, we assess the uncertainty in the ultimate loss amounts required for risk management to ensure financial stability and compliance with insurance regulatory requirements. This study contributes to the field of actuarial science by highlighting the potential of DFMs in enhancing the accuracy and reliability of claim reserving processes.