Skip to main content

Advertisement

Springer Nature Link
Log in

Credit card fraud detection using a deep learning multistage model

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

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

  1. https://www.statista.com/statistics/786778/worldwide-fraud-detection-and-prevention-market-size

  2. https://www.ftc.gov/reports/consumer-sentinel-network-data-book-2019

  3. https://interceptd.com/how-is-machine-learning-used-in-fraud-detection

  4. https://www.netguru.com/blog/fraud-detection-with-machine-learning-banking

  5. Ian Goodfellow, Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press, 2016. http://www.deeplearningbook.org.

  6. Basel, Committee. (2006). International Convergence of Capital Measurement and Capital Standards: A Revised Framework, Comprehensive Version. Switzerland: Bank for International Settlements.

  7. Ian Goodfellow, Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press, 2016. http://www.deeplearningbook.org.

  8. Ian Goodfellow, Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press, 2016. http://www.deeplearningbook.org.

References

  1. Pumsirirat A, Yan L (2018) Credit card fraud detection using deep learning based on auto-encoder and restricted Boltzmann machine. Int J Adv Comput Sci Appl 9(1):18–25. https://doi.org/10.14569/IJACSA.2018.090103 (ISSN 21565570)

    Article  Google Scholar 

  2. Pascal V, Hugo L, Isabelle L, Yoshua B, Pierre-Antoine M (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11(110):3371–3408

    MathSciNet  MATH  Google Scholar 

  3. Valueva MV, Nagornov NN, Lyakhov PA, Valuev GV, Chervyakov NI (2020) Application of the residue number system to reduce hardware costs of the convolutional neural network implementation. Math Comput Simul 177:232–243. https://doi.org/10.1016/j.matcom.2020.04.031

    Article  MathSciNet  MATH  Google Scholar 

  4. Dupond S (2019) A thorough review on the current advance of neural network structures. Annu Rev Control 14:200–230

    Google Scholar 

  5. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE synthetic minority over-sampling technique. J Artif Intell Res 16(February 2017):321–357. https://doi.org/10.1613/jair.953 (ISSN 10769757)

    Article  MATH  Google Scholar 

  6. Han H, Wang WY, Mao BH (2005) Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. Adv Intell Comput 3644:878–887. https://doi.org/10.1007/1153805991

    Article  Google Scholar 

  7. Zeng ZQ, Gao J (2009) Improving SVM Classification with Imbalance Data Set. In: Leung CS, Lee M, Chan JH (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5863. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10677-444

  8. Last F, Douzas G, Bação F (2017) Oversampling for imbalanced learning based on K-Means and SMOTE

  9. He H , Bai Y, Garcia E, Li S (2008) ADASYN: Adaptive Synthetic Sampling Approach for Imbalanced Learning. Proceedings of the International Joint Conference on Neural Networks. 1322–1328. https://doi.org/10.1109/IJCNN.2008.4633969

  10. Prasad NR, Almanza-Garcia S, Lu TT (2009) Anomaly detection. Comput Mater Contin 14(1):1–22. https://doi.org/10.1145/1541880.1541882 (ISSN 15462218)

    Article  Google Scholar 

  11. Iain B, Christophe M (2012) An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Syst Appl 39(3):3446–3453. https://doi.org/10.1016/j.eswa.2011.09.033

    Article  Google Scholar 

  12. Bellotti T, Crook J (2009) Support vector machines for credit scoring and discovery of significant features. Expert Syst Appl 36(2 PART 2):3302–3308. https://doi.org/10.1016/j.eswa.2008.01.005 (ISSN 09574174)

    Article  Google Scholar 

  13. Harris T (2013) Quantitative credit risk assessment using support vector machines: Broad versus Narrow default definitions. Expert Syst Appl 40(11):4404–4413. https://doi.org/10.1016/j.eswa.2013.01.044 (ISSN 09574174)

    Article  Google Scholar 

  14. Barboza F, Kimura H, Altman E (2017) Machine learning models and bankruptcy prediction. Expert Syst Appl 83:405–417. https://doi.org/10.1016/j.eswa.2017.04.006 (ISSN 09574174)

    Article  Google Scholar 

  15. Dal Pozzolo A, Caelen O, Borgne Y, Waterschoot S, Bontempi G (2014) Learned lessons in credit card fraud detection from a practitioner perspective. Expert Syst Appl 41(10):4915–4928. https://doi.org/10.1016/j.eswa.2014.02.026 (ISSN 09574174)

    Article  Google Scholar 

  16. Dal Pozzolo A, Boracchi G, Caelen O, Alippi C, Bontempi G (2018) Credit card fraud detection: a realistic modeling and a novel learning strategy. IEEE Trans Neural Netw Learn Syst 29(8):3784–3797. https://doi.org/10.1109/TNNLS.2017.2736643

    Article  Google Scholar 

  17. Fan Q, Yang J (2018) A Denoising Autoencoder Approach for Credit Risk Analysis. https://doi.org/10.1145/3194452.3194456

  18. Chen J, Shen Y, Ali R (2019) Credit Card Fraud Detection Using Sparse Autoencoder and Generative Adversarial Network. 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2018, (May): 1054–1059. https://doi.org/10.1109/IEMCON.2018.8614815.

  19. Zhu B, Yang W, Wang H, Yuan Y (2018) A hybrid deep learning model for consumer credit scoring. 2018 International Conference on Artificial Intelligence and Big Data, ICAIBD 2018, (May):205–208. https://doi.org/10.1109/ICAIBD.2018.8396195

  20. Wang D et al (2019) “A Semi-Supervised Graph Attentive Network for Financial Fraud Detection,” 2019 IEEE International Conference on Data Mining (ICDM), Beijing, China, pp 598-607, https://doi.org/10.1109/ICDM.2019.00070

  21. Kim A, Cho SB (2019) An ensemble semi-supervised learning method for predicting defaults in social lending. Eng Appl Artif Intel 81:193–199. https://doi.org/10.1016/j.engappai.2019.02.014

    Article  Google Scholar 

  22. Randhawa K, Loo CK, Seera M, Lim CP, Nandi AK (2018) Credit card fraud detection using AdaBoost and majority voting. IEEE Access 6:14277–14284. https://doi.org/10.1109/ACCESS.2018.2806420

    Article  Google Scholar 

  23. Clevert DA (2016) Thomas Unterthiner, and Sepp Hochreiter. Fast and accurate deep network learning by exponential linear units (ELUs). 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings, pages 1–14

  24. Lecun Y, Bottou L, Bengio Y, Haffner P (1998) A B7CEDGF HIB7PRQTSUDGQICWVYX HIB edCdSISIXvg5r ‘CdQTw XvefCdS. proc. OF THE IEEE

  25. Dal Pozzolo A (2015) Adaptive machine learning for credit card fraud detection—Dalpozzolo2015PhD.pdf. (December). URL http://www.ulb.ac.be/di/map/adalpozz/pdf/Dalpozzolo2015PhD.pdf

  26. Dal Pozzolo A, Caelen O, Johnson RA, Bontempi G (2015) Calibrating probability with undersampling for unbalanced classification. Proceedings—2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015, (November): 159–166. https://doi.org/10.1109/SSCI.2015.33

  27. Carcillo F, Dal Pozzolo A, Borgne YL, Caelen O, Mazzer Y, Bontempi G (2018) SCARFF: a scalable framework for streaming credit card fraud detection with spark. Inf Fusion 41(September):182–194. https://doi.org/10.1016/j.inffus.2017.09.005 (ISSN 15662535)

    Article  Google Scholar 

  28. Sperduti A, Navarin N, Oneto L (2020) Recent Advances in Big Data and Deep Learning. Proceedings of the International Neural Networks Society. https://doi.org/10.1007/978-3-030-16841-4

  29. Lebichot B, Borgnee YAL, He-Guelton L, Oble F, Bontempi G (2020b) Recent advances in big data and deep learning. Proceedings of the International Neural Networks Society, 78–88. https://doi.org/10.1007/978-3-030-16841-4

Download references

Funding

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

Author information

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
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Kostas Kolomvatsos
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. George Stamoulis
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

All authors contributed to the study conception and design. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Georgios Zioviris.

Ethics declarations

Conflict of interest

The authors whose names are listed immediately below certify that they have NO affiliations with or involvement in any organization or entity with any financial interest, or non-financial interest in the subject matter or materials discussed in this manuscript.

Ethical approval

Authors confirm that the appropriate ethics review has been followed.

Informed Consent

Not applicable

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-022-04465-9

Keywords