Towards simplified insurance application via sparse questionnaire optimization

Liu, S; Xu, G; Zhu, X; Zhou, Z

Towards simplified insurance application via sparse questionnaire optimization

Liu, S

Xu, G

Zhu, X Zhou, Z

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Publication Type:: Conference Proceeding
Citation:: Proceedings of 4th International Conference on Behavioral, Economic, and Socio-Cultural Computing, BESC 2017, 2017, 2018-January pp. 1 - 2
Issue Date:: 2017-07-01

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Field	Value	Language
dc.contributor.author	Liu, S https://orcid.org/0000-0001-6062-6580	en_US
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.contributor.author	Zhu, X	en_US
dc.contributor.author	Zhou, Z	en_US
dc.date.issued	2017-07-01	en_US
dc.identifier.citation	Proceedings of 4th International Conference on Behavioral, Economic, and Socio-Cultural Computing, BESC 2017, 2017, 2018-January pp. 1 - 2	en_US
dc.identifier.isbn	9781538623657	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122321
dc.description.abstract	© 2017 IEEE. Life insurance application requires in-person meetings with underwriters, tedious paperwork, and an average waiting period of six weeks before an offer can be made. This outdated process has become a barrier for broader consumer adoption, resulting large coverage gap. In this work, we aim to closing this gap by leveraging data mining techniques to optimize the insurance questionnaire form. Our experiment on 10 years of insurance application data has identified that only ∼2% of all questions have shown high relevancy to determining the risks of applicants, resulting a significantly simplified questionnaire.	en_US
dc.relation.ispartof	Proceedings of 4th International Conference on Behavioral, Economic, and Socio-Cultural Computing, BESC 2017	en_US
dc.relation.isbasedon	10.1109/BESC.2017.8256362	en_US
dc.title	Towards simplified insurance application via sparse questionnaire optimization	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-January	en_US
utslib.for	0805 Distributed Computing	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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	2018-January	en_US

Abstract:

© 2017 IEEE. Life insurance application requires in-person meetings with underwriters, tedious paperwork, and an average waiting period of six weeks before an offer can be made. This outdated process has become a barrier for broader consumer adoption, resulting large coverage gap. In this work, we aim to closing this gap by leveraging data mining techniques to optimize the insurance questionnaire form. Our experiment on 10 years of insurance application data has identified that only ∼2% of all questions have shown high relevancy to determining the risks of applicants, resulting a significantly simplified questionnaire.

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

http://hdl.handle.net/10453/122319