Performance of deep neural network for tabular data - a case study of loss cost prediction in fire insurance

Dian Maharani, Hendri Murfi, Yudi Satria

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

The factors that influence fire insurance continue to grow and head to the problem of big data. It is necessary to develop a model to predict the loss cost due to fires by examining the state-of-art models which are adaptable to the big data. One of the models is deep learning, which is an extension of the neural network. This model shows good performances for unstructured data such as image and text. In this paper, we examine the deep learning for loss cost prediction in fire insurance whose training data is tabular or structured data. We use one of the deep learning architectures called deep neural network (DNN), which consists of two or more hidden layers. Our simulation shows that DNN gives quite a similar accuracy to the standard shallow learning of the neural network. It means that deep learning does not improve the performance of the standard shallow learning of neural network for the structured or tabular data of loss cost prediction in fire insurance.

Original languageEnglish
Pages (from-to)734-742
Number of pages9
JournalInternational Journal of Machine Learning and Computing
Volume9
Issue number6
DOIs
Publication statusPublished - 1 Jan 2019

Keywords

  • Deep learning
  • Deep neural network
  • Fire insurance
  • Loss cost prediction
  • Structured data
  • Tabular data

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