Classifying drivers using electronic logging devices

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

doi:10.1109/ICoICT.2017.8074705

Classifying drivers using electronic logging devices

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

School of Mathematical & Computer Sciences

Multimedia University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

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 language	English
Title of host publication	5th International Conference on Information and Communication Technology 2017
Publisher	IEEE
ISBN (Electronic)	9781509049127
DOIs	https://doi.org/10.1109/ICoICT.2017.8074705
Publication status	Published - 19 Oct 2017
Event	5th International Conference on Information and Communication Technology 2017 - Melaka, Malaysia Duration: 17 May 2017 → 19 May 2017

Conference

Conference	5th International Conference on Information and Communication Technology 2017
Abbreviated title	ICoIC7 2017
Country/Territory	Malaysia
City	Melaka
Period	17/05/17 → 19/05/17

Keywords

Classification
Electronic Logging Device and Machine Learning
Naïve Bayes

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems

Access to Document

10.1109/ICoICT.2017.8074705

Cite this

@inproceedings{f87112cc8e6b4e528dce9ac1a537502a,

title = "Classifying drivers using electronic logging devices",

abstract = "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.",

keywords = "Classification, Electronic Logging Device and Machine Learning, Na{\"i}ve Bayes",

author = "Ming, {Low Jia} and Tan, {Ian K. T.} and Hoong, {Poo Kuan}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 5th International Conference on Information and Communication Technology 2017, ICoIC7 2017 ; Conference date: 17-05-2017 Through 19-05-2017",

year = "2017",

month = oct,

day = "19",

doi = "10.1109/ICoICT.2017.8074705",

language = "English",

booktitle = "5th International Conference on Information and Communication Technology 2017",

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address = "United States",

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T1 - Classifying drivers using electronic logging devices

AU - Ming, Low Jia

AU - Tan, Ian K. T.

AU - Hoong, Poo Kuan

PY - 2017/10/19

Y1 - 2017/10/19

N2 - 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.

AB - 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.

KW - Classification

KW - Electronic Logging Device and Machine Learning

KW - Naïve Bayes

UR - http://www.scopus.com/inward/record.url?scp=85039929335&partnerID=8YFLogxK

U2 - 10.1109/ICoICT.2017.8074705

DO - 10.1109/ICoICT.2017.8074705

M3 - Conference contribution

AN - SCOPUS:85039929335

BT - 5th International Conference on Information and Communication Technology 2017

PB - IEEE

T2 - 5th International Conference on Information and Communication Technology 2017

Y2 - 17 May 2017 through 19 May 2017

ER -

Classifying drivers using electronic logging devices

Abstract

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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