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Modeling Insurance Fraud Detection Using Imbalanced Data Classification

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Book cover Advances in Nature and Biologically Inspired Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 419))

Abstract

This paper proposes an innovative insurance fraud detection method to deal with the imbalanced data distribution. The idea is based on building insurance fraud detection models using Decision tree (DT), Support vector machine (SVM) and Artificial Neural Network (ANN), on data partitions derived from under-sampling (with-replacement and without-replacement) of the majority class and merging it with the minority class. Throughout the paper, ten-fold cross validation method of testing is used. Its originality lies in the use of several partitioning under-sampling approaches and choosing the best. Results from a publicly available automobile insurance fraud detection data set demonstrate that DT performs slightly better than other algorithms, so DT model was used to compare between different partitioning-under-sampling approaches. Empirical results illustrate that the proposed model gave better results.

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Author information

Authors and Affiliations

  1. Department of computer science, Sudan University of Science and Technology, Khartoum, Sudan

    Amira Kamil Ibrahim Hassan & Ajith Abraham

  2. Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Amira Kamil Ibrahim Hassan

  3. IT4Innovations, VSB - Technical University of Ostrava, Ostrava, Czech Republic

    Ajith Abraham

Authors
  1. Amira Kamil Ibrahim Hassan
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  2. Ajith Abraham
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Corresponding author

Correspondence to Amira Kamil Ibrahim Hassan .

Editor information

Editors and Affiliations

  1. School of Mathematics, Statistics and CS, University of KwaZulu-Natal, Pietermaritzburg, South Africa

    Nelishia Pillay

  2. Sch of IT, Dept of CS, University of Pretoria, Pretoria, South Africa

    Andries P. Engelbrecht

  3. Sci N/w for Innova and Research Exc, Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  4. Department of Computing Sciences,, Nelson Mandela Metropolitan University, Port Elizabeth, South Africa

    Mathys C. du Plessis

  5. Faculty of Electrical Engineering and, Technical University of Ostrava, Ostrava-Poruba, Czech Republic

    Václav Snášel

  6. Fakulti Teknologi Maklumat dan Komunikas, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malaysia

    Azah Kamilah Muda

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Hassan, A.K.I., Abraham, A. (2016). Modeling Insurance Fraud Detection Using Imbalanced Data Classification. In: Pillay, N., Engelbrecht, A., Abraham, A., du Plessis, M., Snášel, V., Muda, A. (eds) Advances in Nature and Biologically Inspired Computing. Advances in Intelligent Systems and Computing, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-319-27400-3_11

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  • DOI: https://doi.org/10.1007/978-3-319-27400-3_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27399-0

  • Online ISBN: 978-3-319-27400-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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