Automobile Insurance Fraud Detection using Supervised Classifiers

Iffa Maula Nur Prasasti, Arian Dhini, Enrico Laoh

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

Abstract

Automobile fraudulent claim leads to several consequences for the company and policyholder. The current detection system is costly and inefficient. This research aims to design a prediction model in detecting automobile insurance fraud using a machine learning approach. The study used realworld data on an automobile insurance company in Indonesia. The dataset has a high imbalanced distribution between the data of policyholders who commit fraud and legitimate data. This research handles the imbalanced dataset problem by using the Synthetic Minority Oversampling Technique (SMOTE) and undersampling methods. The proposed supervised classifiers are Multilayer Perceptron (MLP), Decision Tree C4.5, and Random Forest(RF). The performance of models is evaluated through the confusion matrix, ROC Curve, and parameters such as sensitivity. This research found that Random Forest outperformed the results comparing to other classifiers with 98.5% accuracy.

Original languageEnglish
Title of host publication2020 International Workshop on Big Data and Information Security, IWBIS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages47-51
Number of pages5
ISBN (Electronic)9781728190983
DOIs
Publication statusPublished - 17 Oct 2020
Event5th International Workshop on Big Data and Information Security, IWBIS 2020 - Depok, Indonesia
Duration: 17 Oct 202018 Oct 2020

Publication series

Name2020 International Workshop on Big Data and Information Security, IWBIS 2020

Conference

Conference5th International Workshop on Big Data and Information Security, IWBIS 2020
CountryIndonesia
CityDepok
Period17/10/2018/10/20

Keywords

  • Automobile Insurance
  • Decision Tree C4.5
  • Fraud Detection
  • Multilayer Perceptron
  • Random Forest
  • SMOTE

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