Classifying drivers using electronic logging devices

Low Jia Ming, Ian K.T. Tan, Poo Kuan Hoong

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

2 Citations (Scopus)


In the era of personalization, being able to determine the risk of individual drivers and hence provide suitable insurance coverage to them would be a logical step. This paper proposes risk scoring for motor insurance using logged data of the drivers that are collected electronically. The proposed method uses machine learning to create a model that can be applied using the logged data. Initial studies conducted were able to achieve up to an accuracy of 79.4%. With further improvement, it can provide a suitable individual risk scoring for insurance premium computation.

Original languageEnglish
Title of host publication2017 5th International Conference on Information and Communication Technology, ICoIC7 2017
EditorsDade Nurjanah
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9781509049127
ISBN (Print)9781509049134
Publication statusPublished - 2017
Externally publishedYes
EventInternational Conference on Information and Communication Technology 2017 - Melaka, Malaysia
Duration: 17 May 201719 May 2017
Conference number: 5th (Proceedings) (Website)


ConferenceInternational Conference on Information and Communication Technology 2017
Abbreviated titleICoICT 2017
Internet address


  • Classification
  • Electronic Logging Device and Machine Learning
  • Naïve Bayes

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