Generating a Risk Profile for Car Insurance Policyholders: A Deep Learning Conceptual Model

Siaminamini, M; Naderpour, M; Lu, J

Generating a Risk Profile for Car Insurance Policyholders: A Deep Learning Conceptual Model

Siaminamini, M

Naderpour, M

Lu, J

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Publisher:: ACIS
Publication Type:: Conference Proceeding
Citation:: Australasian Conference on Information Systems, 2017, pp. 1 - 8
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Siaminamini, M https://orcid.org/0000-0002-9321-3456	en_US
dc.contributor.author	Naderpour, M https://orcid.org/0000-0002-3549-3875	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.date	2017-12-04	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	Australasian Conference on Information Systems, 2017, pp. 1 - 8	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119660
dc.description.abstract	In recent years, technological improvements have provided a variety of new opportunities for insurance companies to adopt telematics devices in line with usage-based insurance models. This paper sheds new light on the application of big data analytics for car insurance companies that may help to estimate the risks associated with individual policyholders based on complex driving patterns. We propose a conceptual framework that describes the structural design of a risk predictor model for insurance customers and combines the value of telematics data with deep learning algorithms. The model’s components consist of data transformation, criteria mining, risk modelling, driving style detection, and risk prediction. The expected outcome is our methodology that generates more accurate results than other methods in this area.	en_US
dc.publisher	ACIS	en_US
dc.relation.ispartof	Australasian Conference on Information Systems	en_US
dc.title	Generating a Risk Profile for Car Insurance Policyholders: A Deep Learning Conceptual Model	en_US
dc.type	Conference Proceeding
utslib.location	Australia	en_US
utslib.location.activity	Hobart, Australia	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	open_access
pubs.consider-herdc	true	en_US
pubs.finish-date	2017-12-06	en_US
pubs.place-of-publication	Australia	en_US
pubs.publication-status	Published	en_US
pubs.start-date	2017-12-04	en_US

Abstract:

In recent years, technological improvements have provided a variety of new opportunities for insurance companies to adopt telematics devices in line with usage-based insurance models. This paper sheds new light on the application of big data analytics for car insurance companies that may help to estimate the risks associated with individual policyholders based on complex driving patterns. We propose a conceptual framework that describes the structural design of a risk predictor model for insurance customers and combines the value of telematics data with deep learning algorithms. The model’s components consist of data transformation, criteria mining, risk modelling, driving style detection, and risk prediction. The expected outcome is our methodology that generates more accurate results than other methods in this area.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/119660