Variable Selection with Machine Learning in the Legalization Process for Traffic Insurance

Vedat Güneş; Serkan Kırca; Hasan Ersan Yagcı; Nida Gokce Narın

doi:10.55549/epstem.1350947

Conference Paper

Variable Selection with Machine Learning in the Legalization Process for Traffic Insurance

Year 2023, Volume: 22, 237 - 246, 01.09.2023

Vedat Güneş , Serkan Kırca Hasan Ersan Yagcı Nida Gokce Narın

https://doi.org/10.55549/epstem.1350947

Abstract

In the insurance sector, the insured notifies the insurance company of which he is the customer as soon as the damage occurs. Upon this notice, a claim file is opened to the insured, and the damage file number is assigned. The claim file contains information about the product insured by the insured and the damage. This information is kept in tables in the databases of Anadolu Insurance. In the event of damage, the insured's claim can be accepted. The entire damage amount can be paid if the damage amount is partially accepted, with the examination to be carried out by the insurance company; if the damage amount is partially accepted, a part of it is paid, or the claim is rejected. The damage amount is not paid at all. When the Insured receives partial payment or the claim file is rejected, they can sue the insurance company to claim the damage amount. The litigation process is a long and bad experience for the insured. For the insurance company, in addition to customer dissatisfaction, it causes extra costs such as court, lawyer, etc. costs. The problem studied in this work is aimed to determine which variables are essential for a possible legalization process in case of partial acceptance or rejection of the claim file by using the variables in the relevant claim file by machine learning and statistical methods. While making this determination, lasso regression, information gain, chi-square test, fisher's score, Recursive Feature Elimination (RFE) with Random Forest Machine Learning algorithm, Univariate Feature Selection with bivariate statistical tests or univariate statistics like chi-square test and feature importance of Random Forest Machine Learning algorithm. Variable selection was made by using correlation coefficient and backward feature elimination methods. Variable p_value was also evaluated.

Keywords

Insurance , Legalization , Anadolu insurance , Variable selection , Machine learning , statistics

References

Akar B. (2021), Müşteriye özel fiyat tahmin çalışması, YBS Ansiklopedi, 9, 13 – 29.
Aker Y. (2022), Comparison of PCA and RFE-RF algorithm in bankruptcy prediction, Gümüşhane University Journal of Social Sciences Institue. 13, 1001 – 1008.
Garcia Y., Garcia B., Gomez M., Fernandez B., & Garcia C. J. (2017), Automatic migraine classification via feature selection committee and machine learning techniques over imaging and questionnaire data, BMC Medical Informatics and Decision Making, 17, 34 – 38.
Gregorutti B., & Michel B. (2017), Correlation and variable importance in random forests, Springer link, 27, 659 – 678.
İlhan A, & Sarı M. H. (2016), Marmara gölü'ndeki (Manisa) vimba vimba (Eğrez) Populasyonunun bazı biyolojik özellikleri. Jurnal of Limnology and Freshwater Fisheries Research, 2, 59 – 65

Year 2023, Volume: 22, 237 - 246, 01.09.2023

Vedat Güneş , Serkan Kırca Hasan Ersan Yagcı Nida Gokce Narın

https://doi.org/10.55549/epstem.1350947

Abstract

References

Akar B. (2021), Müşteriye özel fiyat tahmin çalışması, YBS Ansiklopedi, 9, 13 – 29.
Aker Y. (2022), Comparison of PCA and RFE-RF algorithm in bankruptcy prediction, Gümüşhane University Journal of Social Sciences Institue. 13, 1001 – 1008.
Garcia Y., Garcia B., Gomez M., Fernandez B., & Garcia C. J. (2017), Automatic migraine classification via feature selection committee and machine learning techniques over imaging and questionnaire data, BMC Medical Informatics and Decision Making, 17, 34 – 38.
Gregorutti B., & Michel B. (2017), Correlation and variable importance in random forests, Springer link, 27, 659 – 678.
İlhan A, & Sarı M. H. (2016), Marmara gölü'ndeki (Manisa) vimba vimba (Eğrez) Populasyonunun bazı biyolojik özellikleri. Jurnal of Limnology and Freshwater Fisheries Research, 2, 59 – 65

There are 5 citations in total.

Details

Primary Language	English
Subjects	Software Engineering (Other)
Journal Section	Articles
Authors	Vedat Güneş Serkan Kırca Hasan Ersan Yagcı Nida Gokce Narın
Early Pub Date	August 27, 2023
Publication Date	September 1, 2023
Published in Issue	Year 2023 Volume: 22

Cite

APA	Güneş, V., Kırca, S., Yagcı, H. E., Narın, N. G. (2023). Variable Selection with Machine Learning in the Legalization Process for Traffic Insurance. The Eurasia Proceedings of Science Technology Engineering and Mathematics, 22, 237-246. https://doi.org/10.55549/epstem.1350947