Support Vector Machines for Classifying Policyholders Satisfactorily in Automobile Insurance

Zuherman Rustam, Ni Putu Ayu Audia Ariantari

Research output: Contribution to journalConference articlepeer-review

15 Citations (Scopus)

Abstract

In every insurance company, the satisfactorily of policyholder is important to predict the future of the company. This leads to the point that one needs a system to classify policyholders satisfactorily. In this study, we proposed the used of machine learning, which is Support Vector Machines, to classify policyholders satisfactorily. Thus, we also need to focus on car policyholders' policy. These will be the risk factors that one will classify using Support Vector Machines. By defining risk, insurance company can predict uncertain events easier. If the selected risk factors are adequate then it will ensure the sustainability of the insurance company by avoiding bankruptcy. Hence, several risk factors need to be employed to gain a good explanatory for classifying the policies. It will result in empirical evidence that every insurance company desired most to improve their bottom line. Therefore, Support Vector Machines is claimed to result in a reliable data to classify policyholders satisfactorily.

Original languageEnglish
Article number012005
JournalJournal of Physics: Conference Series
Volume1028
Issue number1
DOIs
Publication statusPublished - 14 Jun 2018
Event2nd International Conference on Statistics, Mathematics, Teaching, and Research 2017, ICSMTR 2017 - Makassar, Indonesia
Duration: 9 Oct 201710 Oct 2017

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