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Flower pollination optimization algorithm with stacked temporal convolution network-based classification for financial anomaly fraud detection

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Abstract

As internet use increases dramatically, an increasing number of industries, including the financial one, are operationalizing their services online. Due to the significant financial losses, they cause that financial fraud is becoming a big issue as it spreads globally and grows in both volume and variety. Financial fraud detection systems should be used to identify dangers like unauthorized access and erratic assaults. During the last several years, this problem has been extensively addressed using machine learning and data mining approaches. These methods still need to be developed in order to cope with massive data, compute quickly, and spot new attack patterns. As a result, in this research, a deep learning-based approach based on the stacked temporal convolution network technique is provided for the identification of financial fraud. This model aims to improve both the efficiency and accuracy of existing detection methods in the context of large data. In addition, a flower pollination optimization process is incorporated for feature selection, which addresses any side effects that may result from choosing the best features. An actual dataset of credit card, loan, and insurance frauds is used to assess the proposed model, and the results are contrasted with those of current deep learning methods. The experimental results show that the suggested FPO_QCNN achieves 99.95%, 99.95%, 99.95% of accuracy for credit card, insurance, and Mortgage Data set.

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The raw data elaborated during the current research are publicly available.

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Authors and Affiliations

  1. Department of CSE, Annamalai University, Chidambaram, India

    N. Krishnavardhan & M. Govindarajan

  2. Department of CSE, Sree Dattha Engineering College, Ibrahimpatnam, India

    S. V. Achutha Rao

Authors
  1. N. Krishnavardhan
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  2. M. Govindarajan
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  3. S. V. Achutha Rao
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Correspondence to N. Krishnavardhan.

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Krishnavardhan, N., Govindarajan, M. & Achutha Rao, S.V. Flower pollination optimization algorithm with stacked temporal convolution network-based classification for financial anomaly fraud detection. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08732-6

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