Analysis Accuracy of Random Forest Model for Big Data - A Case Study of Claim Severity Prediction in Car Insurance

Kartika Chandra Dewi, Hendri Murfi, Sarini Abdullah

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

Insurance claim is one of the important elements in the field of insurance services. Claim severity refers to the amount of fund that must be spent to repair the damage. The amount of insurance claim is influenced by many factors. This causes the volume of data to be very large. Therefore, a suitable method is required. Random Forest, one of the machine learning methods can be implemented to handle this problem. This thesis applies the Random Forest model to predict the amount of this claim severity on car insurance. Furthermore, analysis of the effect of the number of features used on model accuracy is conducted. The simulation result shows that the Random Forest model can be applied in cases of prediction of claim severity, which is a case of regression in the context of machine learning. Only by using 1/3 of the overall features, the accuracy of the Random Forest model can produce accuracy that is comparable to that obtained when using all features which is around 99%. This result confirms the scalability of Random Forest, especially in terms of the number of features. Hence, the Random Forest model can be used as a solution to Big Data problems related to data volume.

Original languageEnglish
Title of host publicationProceeding - 2019 5th International Conference on Science in Information Technology
Subtitle of host publicationEmbracing Industry 4.0: Towards Innovation in Cyber Physical System, ICSITech 2019
EditorsAwang Hendrianto Pratomo, Andri Pranolo, Leonel Hernandez, Rafal Drezewski, Roman Voliansky, Mohamad Shanudin Zakaria, Bagus Muhammad Akbar, Shoffan Saifullah, Ahmad Taufiq Akbar, Rochmat Husaini, Heriyanto Heriyanto, Andiko Putro Suryotomo, Vynska Amalia Permadi, Sylvert Prian Tahalea
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages60-65
Number of pages6
ISBN (Electronic)9781728123806
DOIs
Publication statusPublished - Oct 2019
Event5th International Conference on Science in Information Technology, ICSITech 2019 - Yogyakarta, Indonesia
Duration: 23 Oct 201924 Oct 2019

Publication series

NameProceeding - 2019 5th International Conference on Science in Information Technology: Embracing Industry 4.0: Towards Innovation in Cyber Physical System, ICSITech 2019

Conference

Conference5th International Conference on Science in Information Technology, ICSITech 2019
CountryIndonesia
CityYogyakarta
Period23/10/1924/10/19

Keywords

  • Big Data
  • claim severity prediction
  • ensemble learning
  • machine learning
  • Random Forest
  • scalability

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