TY - GEN
T1 - Automobile Insurance Fraud Detection using Supervised Classifiers
AU - Nur Prasasti, Iffa Maula
AU - Dhini, Arian
AU - Laoh, Enrico
N1 - Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/10/17
Y1 - 2020/10/17
N2 - 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.
AB - 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.
KW - Automobile Insurance
KW - Decision Tree C4.5
KW - Fraud Detection
KW - Multilayer Perceptron
KW - Random Forest
KW - SMOTE
UR - http://www.scopus.com/inward/record.url?scp=85097602961&partnerID=8YFLogxK
U2 - 10.1109/IWBIS50925.2020.9255426
DO - 10.1109/IWBIS50925.2020.9255426
M3 - Conference contribution
AN - SCOPUS:85097602961
T3 - 2020 International Workshop on Big Data and Information Security, IWBIS 2020
SP - 47
EP - 51
BT - 2020 International Workshop on Big Data and Information Security, IWBIS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Workshop on Big Data and Information Security, IWBIS 2020
Y2 - 17 October 2020 through 18 October 2020
ER -