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Credit card fraud detection using a deep learning multistage model

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Abstract

The banking sector is on the eve of a serious transformation and the thrust behind it is artificial intelligence (AI). Novel AI applications have been already proposed to deal with challenges in the areas of credit scoring, risk assessment, client experience and portfolio management. One of the most critical challenges in the aforementioned sector is fraud detection upon streams of transactions. Recently, deep learning models have been introduced to deal with the specific problem in terms of detecting and forecasting possible fraudulent events. The aim is to estimate the unknown distribution of normal/fraudulent transactions and then to identify deviations that may indicate a potential fraud. In this paper, we elaborate on a novel multistage deep learning model that targets to efficiently manage the incoming streams of transactions and detect the fraudulent ones. We propose the use of two autoencoders to perform feature selection and learn the latent data space representation based on a nonlinear optimization model. On the delivered significant features, we subsequently apply a deep convolutional neural network to detect frauds, thus combining two different processing blocks. The adopted combination has the goal of detecting frauds over the exposed latent data representation and not over the initial data.

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Data Availability Statement

The datasets generated analysed during the current study are available in the [https://www.kaggle.com/kartik2112/fraud-detection-banksim/data repository”.

Notes

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The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Thessaly, Glavani 37, 38221, Volos, Greece

    Georgios Zioviris & George Stamoulis

  2. Department of Informatics and Telecommunications, University of Thessaly, Papasiopoulou 2-4, 35131, Lamia, Greece

    Kostas Kolomvatsos

Authors
  1. Georgios Zioviris
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  2. Kostas Kolomvatsos
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  3. George Stamoulis
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All authors contributed to the study conception and design. All authors read and approved the final manuscript.

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Correspondence to Georgios Zioviris.

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Zioviris, G., Kolomvatsos, K. & Stamoulis, G. Credit card fraud detection using a deep learning multistage model. J Supercomput 78, 14571–14596 (2022). https://doi.org/10.1007/s11227-022-04465-9

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