Skip to main content
SpringerLink
Log in

An evaluation of deep learning models for chargeback Fraud detection in online games

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

More and more gamers are willing to pay for games. It has been estimated that the global gaming market is worth nearly US$160 billion. Chargeback services offer gamers the convenience of refund mechanisms but are often used by malicious online gamers to commit fraud, causing huge adverse impacts on the online game industry. To combat chargeback fraud, some online game providers resort to manual checking and blocking of malicious accounts, which may incur huge labor costs in the process. In this research, various deep learning models, including recurrent neural networks, long short-term memory networks, and gated recurrent units, are evaluated on their accuracy and performance in detecting malicious chargebacks in online games. In addition, traditional models, such as decision trees, k-nearest neighbors, support vector machines, and random forests, are also evaluated for comparison. The evaluation results show that the Matthews correlation coefficients of the deep learning models range between 0.84 and 0.97. In addition, the gated recurrent unit and long short-term memory network models also outperform other traditional machine learning models in the experiments in this research. Furthermore, the practical feasibility is also taken into consideration in this research by calculating the time overhead of a single transaction to determine whether there is a significant increase in time costs. Although deep learning models are less efficient than traditional machine learning models, deep learning models remain competent in minimizing losses of online game companies.

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

Access this article

Log in via an institution

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data availability

The datasets generated during and/or analysed during the current study are not publicly available due to the trade secrets consideration.

References

  1. Wijman, T.: Newzoo’s games trends to watch in 2021, 19 (2019)

  2. Intelligence, M.: Gaming industry–growth, trends, and forecast (2020–2025) (2020)

  3. Lee, N., Yoon, H., Choi, D.: Detecting online game chargeback fraud based on transaction sequence modeling using recurrent neural network. In: International Workshop on Information Security Applications, pp. 297–309. Springer

  4. Awoyemi, J.O., Adetunmbi, A.O., Oluwadare, S.A.: Credit card fraud detection using machine learning techniques: a comparative analysis. In: 2017 International Conference on Computing Networking and Informatics (ICCNI), pp. 1–9. IEEE

  5. Seo, J.-H., Choi, D.: Feature selection for chargeback fraud detection based on machine learning algorithms. Int. J. Appl. Eng. Res. 11(22), 10960–10966 (2016)

    Google Scholar 

  6. Chen, X.-W., Wasikowski, M.: Fast: a roc-based feature selection metric for small samples and imbalanced data classification problems. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 124–132

  7. Carneiro, N., Figueira, G., Costa, M.: A data mining based system for credit-card fraud detection in e-tail. Decis. Support Syst. 95, 91–101 (2017)

    Article  Google Scholar 

  8. Mao, H., Liu, Y.-W., Jia, Y., Nanduri, J.: Adaptive fraud detection system using dynamic risk features. arXiv:1810.04654 (2018)

  9. Tedim, M.D.S.: Predicting fraud behaviour in online betting. Thesis (2019)

  10. Pandey, Y.: Credit card fraud detection using deep learning. Int. J. Adv. Res. Comput. Sci. 8(5), 981–984 (2017)

    Google Scholar 

  11. Roy, A., Sun, J., Mahoney, R., Alonzi, L., Adams, S., Beling, P.: Deep learning detecting fraud in credit card transactions. In: 2018 Systems and Information Engineering Design Symposium (SIEDS), pp. 129–134. IEEE

  12. Wiese, B., Omlin, C.: In: Bianchini, M., Maggini, M., Scarselli, F., Jain, L.C. (eds.) Credit card transactions, fraud detection, and machine learning: modelling time with LSTM recurrent neural networks, pp. 231–268. Springer, Berlin, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04003-0_10

  13. Jurgovsky, J., Granitzer, M., Ziegler, K., Calabretto, S., Portier, P.-E., He-Guelton, L., Caelen, O.: Sequence classification for credit-card fraud detection. Expert Syst. Appl. 100, 234–245 (2018)

    Article  Google Scholar 

  14. Najadat, H., Altiti, O., Aqouleh, A.A., Younes, M.: Credit card fraud detection based on machine and deep learning. In: 2020 11th International Conference on Information and Communication Systems (ICICS), pp. 204–208. IEEE

  15. Heryadi, Y., Warnars, H.L.H.S.: Learning temporal representation of transaction amount for fraudulent transaction recognition using CNN, Stacked LSTM, and CNN-LSTM. In: 2017 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom), pp. 84–89 (2017). https://doi.org/10.1109/CYBERNETICSCOM.2017.8311689

  16. Fu, K., Cheng, D., Tu, Y., Zhang, L.: Credit card fraud detection using convolutional neural networks. In: International Conference on Neural Information Processing, pp. 483–490. Springer

  17. Zhang, Z., Zhou, X., Zhang, X., Wang, L., Wang, P.: A model based on convolutional neural network for online transaction fraud detection. Secur. Commun. Netw. 2018 (2018)

  18. Abakarim, Y., Lahby, M., Attioui, A.: An efficient real time model for credit card fraud detection based on deep learning. In: Proceedings of the 12th International Conference on Intelligent Systems: Theories and Applications, pp. 1–7

  19. Pumsirirat, A., Yan, L.: 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 (2018)

    Google Scholar 

  20. Rushin, G., Stancil, C., Sun, M., Adams, S., Beling, P.: Horse race analysis in credit card fraud—deep learning, logistic regression, and gradient boosted tree. In: 2017 Systems and Information Engineering Design Symposium (SIEDS), pp. 117–121. IEEE

  21. Uçar, M.: Classification performance-based feature selection algorithm for machine learning: P-score. IRBM (2020)

  22. Claypo, N., Jaiyen, S.: A new feature selection based on class dependency and feature dissimilarity. In: 2015 2nd International Conference on Advanced Informatics: Concepts, Theory and Applications (ICAICTA), pp. 1–6. IEEE

  23. Pant, H., Srivastava, R.: A survey on feature selection methods for imbalanced datasets. Int. J. Comput. Eng. Appl. 9(2), 197–204 (2015)

    Google Scholar 

  24. Lai, Y.-X., Liao, T.-Y., Wu, Y.-S., Wei, Y.-C.: Based on Genetic Algorithm for Feature Selection of Chargeback Fraud Detection in Online Games (2020)

  25. Elman, J.L.: Finding structure in time. Cogn. Sci. 14(2), 179–211 (1990)

    Article  Google Scholar 

  26. Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv:1412.3555 (2014)

  27. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  28. Graves, A.: Long Short-Term Memory, pp. 37–45. Springer, Berlin, Heidelberg (2012). https://doi.org/10.1007/978-3-642-24797-2_4

  29. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv:1409.0473 (2014)

  30. Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998). https://doi.org/10.1109/5.726791

    Article  Google Scholar 

  31. Chicco, D., Jurman, G.: The advantages of the Matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation. BMC Genomics 21(1), 1–13 (2020)

    Article  Google Scholar 

  32. Mathew, J., Pang, C.K., Luo, M., Leong, W.H.: Classification of imbalanced data by oversampling in kernel space of support vector machines. IEEE Trans. Neural Netw. Learn. Syst. 29(9), 4065–4076 (2018). https://doi.org/10.1109/TNNLS.2017.2751612

    Article  Google Scholar 

  33. Zheng, Z., Cai, Y., Li, Y.: Oversampling method for imbalanced classification. Comput. Inf. 34(5), 1017–1037 (2015)

    Google Scholar 

  34. Aridas, C.K., Karlos, S., Kanas, V.G., Fazakis, N., Kotsiantis, S.B.: Uncertainty based under-sampling for learning Naive Bayes classifiers under imbalanced data sets. IEEE Access 8, 2122–2133 (2020). https://doi.org/10.1109/ACCESS.2019.2961784

    Article  Google Scholar 

  35. Lin, W.-C., Tsai, C.-F., Hu, Y.-H., Jhang, J.-S.: Clustering-based undersampling in class-imbalanced data. Inf. Sci. 409–410, 17–26 (2017). https://doi.org/10.1016/j.ins.2017.05.008

    Article  Google Scholar 

  36. Agrawal, A., Viktor, H.L., Paquet, E.: Scut: Multi-class imbalanced data classification using smote and cluster-based undersampling. In: 2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K), vol. 01, pp. 226–234 (2015)

  37. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002). https://doi.org/10.1613/jair.953

    Article  MATH  Google Scholar 

Download references

Acknowledgements

This research was partially funded by Ministry of Science and Technology (No. 110-2637-H-027-004-). We would like to thank the anonymous reviewers for their valuable feedback. We are also grateful to Lien Wang and another anonymous individual for proofreading the manuscript.

Funding

This work was partially supported by Ministry of Science and Technology, Taiwan (Grant Numbers 110-2637-H-027-004-).

Author information

Authors and Affiliations

  1. Department of Information and Finance Management, National Taipei University of Technology, Taipei, 10608, Taiwan R.O.C.

    Yu-Chih Wei, You-Xin Lai & Mu-En Wu

Authors
  1. Yu-Chih Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. You-Xin Lai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mu-En Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by YCW, YXL and MEW. The first draft of the manuscript was written by YCW and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yu-Chih Wei.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

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

Wei, YC., Lai, YX. & Wu, ME. An evaluation of deep learning models for chargeback Fraud detection in online games. Cluster Comput 26, 927–943 (2023). https://doi.org/10.1007/s10586-022-03674-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-022-03674-4

Keywords

Search

Navigation