BERT-based NLP techniques for classification and severity modeling in basic warranty data study

Abstract

This paper is to explore data-driven models based on a newly developed natural language processing (NLP) tool called Bidirectional Encoder Representations from Transformer (BERT) to incorporate textural data information for group classification and loss amount prediction on truck's basic warranty claims. In group classification modeling, multiple-class logistic regression is compared with BERT-based back-propagation neural networks (NN). In group loss severity modeling, direct NN regression is compared with BERT-based NN regression prediction. Furthermore, based on the results from a so-called optimal bin-width algorithm, the severity distribution is fitted in Gamma and its parameters are then estimated using maximum likelihood estimation (MLE). The data experiments show that the BERT framework for NLP improves the classification of the warranty claims as well as the loss severity prediction both in accuracy and stability.