Explainable Credit Card Fraud Detection with Image Conversion

  • Duygu Sinanc
  • Umut Demirezen
    Artificial Intelligence and Big Data Unit, Digital Transformation Office
  • Şeref Sağıroğlu
    Gazi University


The increase in the volume and velocity of credit card transactions causes class imbalance and concept deviation problems in data sets where credit card fraud is detected. These problems make it very difficult for traditional approaches to produce robust detection models. In this study, a different perspective has been developed for this problem and a novel approach named Fraud Detection with Image Conversion (FDIC) is proposed. FDIC handles credit card transactions as time series and transforms them into images. These images, which comprise temporal correlations and bilateral relationships of features, are classified by a convolutional neural network architecture as fraudulent or legitimate. When the obtained results are compared with the related studies, FDIC has the best F1-score and recall values, which are 85.49% and 80.35%, respectively. Since the images created during the FDIC process are difficult to interpret, a new explainable artificial intelligence approach is also presented. In this way, feature relationships that have a dominant effect on fraud detection are revealed.
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Abakarim, Y., Lahby, M., and Attioui, A. (2018). An Efficient Real Time Model For Credit Card Fraud Detection Based On Deep Learning. ACM International Conference on Intelligent Systems: Theories and Applications, Rabat, Morocco. 1-7.

Adadi, A., and Berrada, M. (2018). Peeking inside the black-box: A survey on Explainable Artificial Intelligence (XAI). IEEE Access, 6, 52138-52160.

Adewumi, A. O., and Akinyelu, A. A. (2017). A survey of machine-learning and nature-inspired based credit card fraud detection techniques. International Journal of System Assurance Engineering and Management, 8(2), 937-953.

Al-Shabi, M. A. (2019). Credit Card Fraud Detection Using Autoencoder Model in Unbalanced Datasets. Journal of Advances in Mathematics and Computer Science, 33(5), 1-16.

Arif, M., and Dar, A. R. (2015). Survey on fraud detection techniques using data mining. International Journal of u-and e-Service, Science and Technology, 8(3), 165-170.

Bagnall, A., Lines, J., Bostrom, A., Large, J., and Keogh, E. (2017). The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Mining and Knowledge Discovery, 31(3), 606-660.

Behdad, M., Barone, L., Bennamoun, M., and French, T. (2012). Nature-inspired techniques in the context of fraud detection. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 42(6), 1273-1290.

Clevert, D. A., Unterthiner, T., and Hochreiter, S. (2015). Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289.

Estebsari, A., and Rajabi, R. (2020). Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques. Electronics, 9(68), 1-17.

Fawaz, H. I., Forestier, G., Weber, J., Idoumghar, L., and Muller, P. A. (2019). Deep learning for time series classification: a review. Data Mining and Knowledge Discovery, 33(4), 917-963.

Fu, T. C. (2011). A review on time series data mining. Engineering Applications of Artificial Intelligence, 24(1), 164-181.

Gold, S. (2014). The evolution of payment card fraud. Computer Fraud & Security, 2014(3), 12-17.

Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., and Chen, T. (2018). Recent advances in convolutional neural networks. Pattern Recognition, 77, 354-377.

Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., and Pedreschi, D. (2018). A survey of methods for explaining black box models. ACM computing surveys (CSUR), 51(5), 1-42.

Guo, H., Liu, H., Wu, C., Zhi, W., Xiao, Y., and She, W. (2016). Logistic discrimination based on G-mean and F-measure for imbalanced problem. Journal of Intelligent and Fuzzy Systems, 31(3), 1155-1166.

Gupta, D., and Rani, R. (2019). A study of big data evolution and research challenges. Journal of Information Science, 45(3), 322-340.

Hong, Y. Y., Martinez, J. J. F., and Fajardo, A. C. (2020). Day-Ahead Solar Irradiation Forecasting Utilizing Gramian Angular Field and Convolutional Long Short-Term Memory. IEEE Access, 8, 18741-18753.

Hsueh, Y., Ittangihala, V. R., Wu, W. B., Chang, H. C., and Kuo, C. C. (2019). Condition monitor system for rotation machine by CNN with recurrence plot. Energies, 12(3221), 1-13.

Kingma, D. P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.

Liu, X., Cai, H., Zhong, R., Sun, W., and Chen, J. (2020). Learning Traffic as Images for Incident Detection Using Convolutional Neural Networks. IEEE Access, 8, 7916-7924.

Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., and Johnson, B. A. (2019). Deep learning in remote sensing applications: A meta-analysis and review. ISPRS journal of photogrammetry and remote sensing, 152, 166-177.

Maimon, O., and Rokach, L. (2005). Data mining and knowledge discovery handbook, Boston: Springer, 1069-1103.

Mohammed, R. A., Wong, K. W., Shiratuddin, M. F., and Wang, X. (2018). Scalable machine learning techniques for highly imbalanced credit card fraud detection: a comparative study. Pacific Rim International Conference on Artificial Intelligence, Nanjing, China, 237-246.

Nilson Report 1164. Retrieved on December 1, 2020, from: https://nilsonreport.com/publication_newsletter_archive_issue.php?issue=1164.

Qin, Z., Zhang, Y., Meng, S., Qin, Z., and Choo, K. K. R. (2020). Imaging and fusing time series for wearable sensor-based human activity recognition. Information Fusion, 53, 80-87.

Rusiecki, A. (2019). Trimmed categorical cross-entropy for deep learning with label noise. Electronics Letters, 55(6), 319-320.

Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017). Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision, 618-626.

Seyedhossein, L., and Hashemi, M. R. (2010). A Timelier Credit Card Fraud Detection by Mining Transaction Time Series. International Journal of Information and Communication Technology, 2(3), 21-28.

Université Libre de Bruxelles (ULB) Machine Learning Group, Credit Card Fraud Detection Dataset, Retrieved on December 1, 2020, from: https://www.kaggle.com/mlg-ulb/creditcardfraud.

Wang, Z., and Oates, T. (2015a). Imaging time-series to improve classification and imputation. ACM International Conference on Artificial Intelligence, Buenos Aires, Argentina, 3939-3945.

Wang, Z., and Oates, T. (2015b). Encoding time series as images for visual inspection and classification using tiled convolutional neural networks. Association for the Advancement of Artificial Intelligence Conference on Artificial Intelligence, Austin, United States, 1-7.

Wilson, S. J. (2017). Data representation for time series data mining: time domain approaches. Wiley Interdisciplinary Reviews: Computational Statistics, 9, 1-6.

Xuan, S., Liu, G., Li, Z., Zheng, L., Wang, S., and Jiang, C. (2018). Random forest for credit card fraud detection. IEEE International Conference on Networking, Sensing and Control, Zhuhai, China, 1-6.

Zhang, R., Zheng, F., and Min, W. (2018). Sequential Behavioral Data Processing Using Deep Learning and the Markov Transition Field in Online Fraud Detection. ACM SIGKDD Conference on Knowledge Discovery and Data Mining Data Science in Fintech Workshop, 1-5.
Sinanc, D., Demirezen, U., & Sağıroğlu, Şeref . (2021). Explainable Credit Card Fraud Detection with Image Conversion . ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 10(1), 63–76. https://doi.org/10.14201/ADCAIJ20211016376


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Author Biography

Şeref Sağıroğlu

Gazi University