Improving insurers’ loss reserve error prediction: Adopting combined unsupervised-supervised machine learning techniques in risk management

Abstract

Emerging literature focuses on insurers' earnings management using estimated liability for unpaid claims, known as loss reserve. An insurance company generally uses the traditional estimation methods with linear estimation to measure loss reserve error, but those methods are often criticized for several statistical shortcomings, such as estimation technique, correlated contributing variables, ignorance of the interactions, and higher-order terms. To overcome such shortcomings, this paper proposes an unsupervised-supervised machine learning approach, hierarchical clustering, and artificial neural network (ANN) by adopting a combined unsupervised-supervised method, cluster analysis (i.e., unsupervised), and various supervised machine learning algorithms such as Boostings, Support Vector Machine (SVM) and RReliefF. We show evidence that each cluster has its own foundation variables to predict and Boosting and ANN estimation provide a more efficient framework to improve insurers' reserve error. Also, the different value and order of RReliefF between Boosting and OLS show the under-or over-estimated predictor, and each year's influential variables are found to be consistent over time, which indicates that the firm's previous year's loss reserve model can predict the future loss reserve error. This paper contributes to the existing literature by suggesting a more robust, consistent, and efficient prediction method (i.e., unsupervised-supervised combination method) to improve insurers' loss reserve error prediction.