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Using a Data Mining Approach to Detect Automobile Insurance Fraud

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Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021) (SoCPaR 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 417))

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Abstract

The number of policyholders involved in fraudulent activities has increased dramatically in recent years. Intentionally misleading insurers by missing facts when claiming insurance has resulted in massive losses for insurers. There is indeed a lack of a robust system for rigorously addressing insurance fraud. Throughout the paper, we presented a data mining approach to detect fraudulent claims. Following two sampling methods (SMOTE, ROSE) to remove the class imbalance and experimenting with two different features subsets (the first composed of 23 predictors, the second of 5 predictors), we employed Random Forests and Logistic Regression. For validation, we experimented with a (75:25) split ratio for a real dataset of automobile insurance claims to test the performance of our proposed models. The results revealed that the models built using the second feature selection perform slightly better with a higher rate of correctly classified fraudulent claims (Random Forest recall = 95.24%); further, statistically, there is an insignificant difference between SMOTE and ROSE. Finally, the study has demonstrated that random forest outperforms logistic regression.

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Author information

Authors and Affiliations

  1. National School of Statistics and Applied Economics, Tipaza, Algeria

    Mabrouka Salmi

  2. University of Tipaza, Tipaza, Algeria

    Dalia Atif

Authors
  1. Mabrouka Salmi
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  2. Dalia Atif
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Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Department of Industrial Engineering and Computer Science, Stellenbosch University, Matieland, South Africa

    Andries Engelbrecht

  3. Department of Computer Science, Università degli Studi di Milano, Milan, Italy

    Fabio Scotti

  4. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  5. University of Mumbai, Mumbai, Maharashtra, India

    Pooja Manghirmalani Mishra

  6. University of Calabria (Unical), Rende, Italy

    Giancarlo Fortino

  7. Department of Informatics, Vilnius University, Kaunas, Lithuania

    Virgilijus Sakalauskas

  8. Center for Smart Computing Continuum, Forschung Burgenland, Eisenstadt, Austria

    Sabri Pllana

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Salmi, M., Atif, D. (2022). Using a Data Mining Approach to Detect Automobile Insurance Fraud. In: Abraham, A., et al. Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021). SoCPaR 2021. Lecture Notes in Networks and Systems, vol 417. Springer, Cham. https://doi.org/10.1007/978-3-030-96302-6_5

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