Affiliation:
1. Department of Actuarial Mathematics and Statistics, School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK
2. Teva Pharmaceuticals, 1407 Sofia, Bulgaria
Abstract
We developed a methodology for the neural network boosting of logistic regression aimed at learning an additional model structure from the data. In particular, we constructed two classes of neural network-based models: shallow–dense neural networks with one hidden layer and deep neural networks with multiple hidden layers. Furthermore, several advanced approaches were explored, including the combined actuarial neural network approach, embeddings and transfer learning. The model training was achieved by minimizing either the deviance or the cross-entropy loss functions, leading to fourteen neural network-based models in total. For illustrative purposes, logistic regression and the alternative neural network-based models we propose are employed for a binary classification exercise concerning the occurrence of at least one claim in a French motor third-party insurance portfolio. Finally, the model interpretability issue was addressed via the local interpretable model-agnostic explanations approach.
Subject
Computational Mathematics,Computational Theory and Mathematics,Numerical Analysis,Theoretical Computer Science
Reference53 articles.
1. Parodi, P. (2014). Pricing in General Insurance, CRC Press.
2. Wüthrich, M.V., Buser, C., and Data Analytics for Non-Life Insurance Pricing (2023, January 30). Swiss Finance Institute Research Paper 2020. Available online: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2870308.
3. AI in actuarial science—A review of recent advances—Part 1;Richman;Ann. Actuar. Sci.,2020
4. AI in actuarial science—A review of recent advances—Part 2;Richman;Ann. Actuar. Sci.,2020
5. Yes, we CANN!;Merz;ASTIN Bull. J. IAA,2019
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献