Application of Deep Learning and Neural Network to Speeding Ticket and Insurance Claim Count Data

Jong Min Kim, Jihun Kim, Il Do Ha

Research output: Contribution to journalArticlepeer-review

Abstract

With the popularity of big data analysis with insurance claim count data, diverse regression models for count response variable have been developed. However, there is a multicollinearlity issue with multivariate input variables to the count response regression models. Recently, deep learning and neural network models for count response have been proposed, and a Keras and Tensorflow-based deep learning model has been also proposed. To apply the deep learning and neural network models to non-normal insurance claim count data, we perform the root mean square error accuracy comparison of gradient boosting machines (a popular machine learning regression tree algorithm), principal component analysis (PCA)-based Poisson regression, PCA-based negative binomial regression, and PCA-based zero inflated poisson regression to avoid the multicollinearity of multivariate input variables with the simulated normal distribution data and the non-normal simulated data combined with normally distributed data, binary data, copula-based asymmetrical data, and two real data sets, which consist of speeding ticket and Singapore insurance claim count data.

Original languageEnglish (US)
Article number280
JournalAxioms
Volume11
Issue number6
DOIs
StatePublished - Jun 2022

Bibliographical note

Funding Information:
Funding: National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. NRF-2020R1F1A1A01056987).

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • deep learning
  • negative binomial regression
  • poisson
  • zero inflated poisson

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