Ensemble distributional forecasting for insurance loss reserving

TL;DR

This paper introduces a novel ensemble framework for stochastic loss reserving that leverages distributional properties and reserving data features, improving predictive accuracy over traditional methods.

Contribution

It proposes a systematic, distribution-aware ensemble method tailored for reserving data, with an accompanying R package for implementation.

Findings

Ensemble outperforms traditional model selection and equal weighting.

Improves accuracy of both central estimates and quantiles.

Framework effectively captures distributional aspects of reserves.

Abstract

Loss reserving generally focuses on identifying a single model that can generate superior predictive performance. However, different loss reserving models specialise in capturing different aspects of loss data. This is recognised in practice in the sense that results from different models are often considered, and sometimes combined. For instance, actuaries may take a weighted average of the prediction outcomes from various loss reserving models, often based on subjective assessments. In this paper, we propose a systematic framework to objectively combine (i.e. ensemble) multiple _stochastic_ loss reserving models such that the strengths offered by different models can be utilised effectively. Our framework contains two main innovations compared to existing literature and practice. Firstly, our criteria model combination considers the full distributional properties of the ensemble and not just the central estimate - which is of particular importance in the reserving context. Secondly, our framework is that it is tailored for the features inherent to reserving data. These include, for instance, accident, development, calendar, and claim maturity effects. Crucially, the relative importance and scarcity of data across accident periods renders the problem distinct from the traditional ensembling techniques in statistical learning. Our framework is illustrated with a complex synthetic dataset. In the results, the optimised ensemble outperforms both (i) traditional model selection strategies, and (ii) an equally weighted ensemble. In particular, the improvement occurs not only with central estimates but also relevant quantiles, such as the 75th percentile of reserves (typically of interest to both insurers and regulators). The framework developed in this paper can be implemented thanks to an R package, `ADLP`, which is available from CRAN.