Journal
INSURANCE MATHEMATICS & ECONOMICS
Volume 107, Issue -, Pages 57-67Publisher
ELSEVIER
DOI: 10.1016/j.insmatheco.2022.07.013
Keywords
BERT; Classification; Data-driven; Loss severity; NLP; NN-regression; Warranty policy pricing
Categories
Funding
- [C22-0005]
Ask authors/readers for more resources
This paper explores data-driven models based on BERT for group classification and loss amount prediction on truck's basic warranty claims. The experiments show that the BERT framework improves the accuracy and stability of classification and severity prediction.
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.(c) 2022 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available