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Anomaly Detection Using Modified Differential Evolution: An Application to Banking and Insurance

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Proceedings of the 11th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2019) (SoCPaR 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1182))

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Abstract

We propose two Modified Differential Evolution driven subspace based optimization models for anomaly detection in customer credit card churn detection, automobile insurance fraud detection and customer credit card default detection. Sparsity coefficient is chosen as the objective function for discovering anomalies. Also, we employed an external performance measure as selection constraint, namely, precision multiplied by recall at every iteration after a pre-specified iteration count. The proposed technique outperformed a bunch of baseline algorithms for anomaly detection, for example, Local Outlier Factor, Angle based Outlier Detection, K-means, Partition Around Medoids and also the proposed model without invoking the external performance measure in terms of precision and Area Under ROC Curve (AUC) indicating that the proposed method a viable alternative for anomaly detection.

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Authors and Affiliations

  1. Center of Excellence in Analytics, Institute for Development and Research in Banking Technology, Hyderabad, 500057, India

    Gutha Jaya Krishna & Vadlamani Ravi

  2. School of Computer and Information Sciences, University of Hyderabad, Hyderabad, 500046, India

    Gutha Jaya Krishna

Authors
  1. Gutha Jaya Krishna
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  2. Vadlamani Ravi
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Corresponding author

Correspondence to Vadlamani Ravi .

Editor information

Editors and Affiliations

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

    Ajith Abraham

  2. Department of Computer Science and Engineering, Vardhaman College of Engineering, Hyderabad, Telangana, India

    M. A. Jabbar

  3. Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid, Spain

    Sanju Tiwari

  4. ISEP - Instituto Superior de Engenharia do Porto, Porto, Portugal

    Isabel M. S. Jesus

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Jaya Krishna, G., Ravi, V. (2021). Anomaly Detection Using Modified Differential Evolution: An Application to Banking and Insurance. In: Abraham, A., Jabbar, M., Tiwari, S., Jesus, I. (eds) Proceedings of the 11th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2019). SoCPaR 2019. Advances in Intelligent Systems and Computing, vol 1182. Springer, Cham. https://doi.org/10.1007/978-3-030-49345-5_11

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