Predicting automobile insurance fraud using classical and machine learning models
Insurance fraud claims have become a major problem in the insurance industry. Several investigations have been carried out to eliminate negative impacts on the insurance industry as this immoral act has caused the loss of billions of dollars. In this paper, a comparative study was carried out to ass...
Published in: | International Journal of Electrical and Computer Engineering |
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Institute of Advanced Engineering and Science
2024
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2-s2.0-85183879321 Nordin S.-Z.S.; Wah Y.B.; Haur N.K.; Hashim A.; Norimah R.; Jalil N.A. Predicting automobile insurance fraud using classical and machine learning models 2024 International Journal of Electrical and Computer Engineering 14 1 10.11591/ijece.v14i1.pp911-921 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183879321&doi=10.11591%2fijece.v14i1.pp911-921&partnerID=40&md5=c99e8cbe137f99f5b093dd96d54d97ac Insurance fraud claims have become a major problem in the insurance industry. Several investigations have been carried out to eliminate negative impacts on the insurance industry as this immoral act has caused the loss of billions of dollars. In this paper, a comparative study was carried out to assess the performance of various classification models, namely logistic regression, neural network (NN), support vector machine (SVM), tree augmented naïve Bayes (NB), decision tree (DT), random forest (RF) and AdaBoost with different model settings for predicting automobile insurance fraud claims. Results reveal that the tree augmented NB outperformed other models based on several performance metrics with accuracy (79.35%), sensitivity (44.70%), misclassification rate (20.65%), area under curve (0.81) and Gini (0.62). In addition, the result shows that the AdaBoost algorithm can improve the classification performance of the decision tree. These findings are useful for insurance professionals to identify potential insurance fraud claim cases. © 2024 Institute of Advanced Engineering and Science. All rights reserved. Institute of Advanced Engineering and Science 20888708 English Article All Open Access; Gold Open Access |
author |
Nordin S.-Z.S.; Wah Y.B.; Haur N.K.; Hashim A.; Norimah R.; Jalil N.A. |
spellingShingle |
Nordin S.-Z.S.; Wah Y.B.; Haur N.K.; Hashim A.; Norimah R.; Jalil N.A. Predicting automobile insurance fraud using classical and machine learning models |
author_facet |
Nordin S.-Z.S.; Wah Y.B.; Haur N.K.; Hashim A.; Norimah R.; Jalil N.A. |
author_sort |
Nordin S.-Z.S.; Wah Y.B.; Haur N.K.; Hashim A.; Norimah R.; Jalil N.A. |
title |
Predicting automobile insurance fraud using classical and machine learning models |
title_short |
Predicting automobile insurance fraud using classical and machine learning models |
title_full |
Predicting automobile insurance fraud using classical and machine learning models |
title_fullStr |
Predicting automobile insurance fraud using classical and machine learning models |
title_full_unstemmed |
Predicting automobile insurance fraud using classical and machine learning models |
title_sort |
Predicting automobile insurance fraud using classical and machine learning models |
publishDate |
2024 |
container_title |
International Journal of Electrical and Computer Engineering |
container_volume |
14 |
container_issue |
1 |
doi_str_mv |
10.11591/ijece.v14i1.pp911-921 |
url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183879321&doi=10.11591%2fijece.v14i1.pp911-921&partnerID=40&md5=c99e8cbe137f99f5b093dd96d54d97ac |
description |
Insurance fraud claims have become a major problem in the insurance industry. Several investigations have been carried out to eliminate negative impacts on the insurance industry as this immoral act has caused the loss of billions of dollars. In this paper, a comparative study was carried out to assess the performance of various classification models, namely logistic regression, neural network (NN), support vector machine (SVM), tree augmented naïve Bayes (NB), decision tree (DT), random forest (RF) and AdaBoost with different model settings for predicting automobile insurance fraud claims. Results reveal that the tree augmented NB outperformed other models based on several performance metrics with accuracy (79.35%), sensitivity (44.70%), misclassification rate (20.65%), area under curve (0.81) and Gini (0.62). In addition, the result shows that the AdaBoost algorithm can improve the classification performance of the decision tree. These findings are useful for insurance professionals to identify potential insurance fraud claim cases. © 2024 Institute of Advanced Engineering and Science. All rights reserved. |
publisher |
Institute of Advanced Engineering and Science |
issn |
20888708 |
language |
English |
format |
Article |
accesstype |
All Open Access; Gold Open Access |
record_format |
scopus |
collection |
Scopus |
_version_ |
1809677573441978368 |