Implementation of Interpolation in Credit Card Fraud Detection

  • Pranali ShenviEmail author
  • Neel Samant
  • Shubham Kumar
  • Vaishali KulkarniEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1118)


In today’s world, a critical threat to the Banking and Finance sector as well as its customers is the occurrence of fraud in the credit card transactions. Detecting this fraud is extremely arduous as it forms only a small percentage of the total number of transactions. In this paper, an algorithm which uses artificial neural network to detect fraudulent transactions amongst numerous genuine ones has been proposed. The number of fraudulent transactions is very few in comparison with the genuine transactions which introduces skewness in the data and makes the task of fraud detection difficult. In order to reduce the skewness of the dataset, under-sampling and over-sampling techniques have been used. The algorithm has been tested on three different datasets. The results of all the datasets have been compared. Confusion matrix and ROC plots have been compared. Further to improve the classification, generative adversarial networks (GANs) have been used to interpolate the fraudulent data without duplication on one of the datasets. These results show that there is an improvement in classification by using GANs.


Fraud detection Neural networks Standardization Interpolation Generative adversarial networks 



We would like to acknowledge Andrea Dal Pozzolo et al. for the credit card dataset, Dr. Kohei Hayashi, Nara Institute of Science and Technology, Japan, for sending us the UCSD FICO Data mining contest 2009 Dataset as well as Edgar Alonso Lopez-Rojas for providing us with the simulated dataset Paysim.


  1. 1.
    Shenvi, P., Samant, N., Kumar, S., Kulkarni, V.: Credit card fraud detection using deep learning. In: IEEE 5th I2CT 2019, (accepted for presentation) (2019)Google Scholar
  2. 2.
    Dal Pozzolo, A., Caelen, O., Johnson, R.A., Bontempi, G.: Calibrating probability with undersampling for unbalanced classification. In: Symposium on Computational Intelligence and Data Mining (CIDM), IEEE (2015)Google Scholar
  3. 3.
    Liu, Y., Zhou, Y., Liu, X., Dong, F., Wang, C., Wang, Z.: Wasserstein GAN-based small-sample augmentation for new-generation Artificial Intelligence: a case study of cancer-staging data in Biology. Engineering 5(1), 156–163 (2019). (ISSN 2095-8099)CrossRefGoogle Scholar
  4. 4.
    Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Proceedings of the 27th International Conference on Neural Information Processing Systems, Montreal, Canada, pp. 2672–2680, 08–13 Dec 2014Google Scholar
  5. 5.
    Renu, S.: Analysis on credit card fraud detection methods. Int. J. Comput. Trends Technol. (IJCTT) 8(1), 45–51 (2014)CrossRefGoogle Scholar
  6. 6.
    Dhankhad, S., Behrouz Far and Emad A. Mohammed.: Supervised Machine Learning Algorithms for Credit Card Fraudulent Transaction Detection: A Comparative Study”, 2018 IEEE International Conference on Information Reuse and Integration (IRI), Salt Lake City, UT, 2018, pp. 122–125.
  7. 7.
    Awoyemi, J.O., Adetunmbi, A.O., Oluwadare, S.A.: Credit card fraud detection using Machine Learning Techniques: A comparative analysis. (2017)
  8. 8.
    Lopez-Rojas, E.A.: Applying Simulation to the Problem of Detecting Financial Fraud”, Ph.D. dissertation, Karlskrona (2016)Google Scholar
  9. 9.
    Seeja, K.R., Zareapoor, M.: FraudMiner: a novel credit card fraud detection model based on frequent itemset mining. Sci. World J. vol. 2014, Article ID 252797, p. 10 (2014)Google Scholar
  10. 10.
    Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein gan. arXiv preprint arXiv:1701.07875 (2017)
  11. 11.
  12. 12.
    Shrivastava, A., Yadav, M., Basu, S., Salunkhe, S., Shabad, M.: Credit card fraud detection at merchant side using neural network. In: 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, pp. 667–670 (2016)Google Scholar
  13. 13.
    Jiang, C., Song, J., Liu, G., Zheng, L., Luan, W.: Credit card fraud detection: a novel approach using aggregation strategy and feedback mechanism. IEEE Internet of Things J. 5(5), 3637–3647 (2018). Scholar
  14. 14.
    Patil, S., Nemade, V., Kumar, S., Piyush.: Predictive modelling for credit card fraud detection using data analytics. Procedia Comput. Sci. 132, 385–395. (2018)
  15. 15.
    Salazar, A., Safont, G., Vergara, L.: Surrogate techniques for testing fraud detection algorithms in credit card operations. In: 2014 International Carnahan Conference on Security Technology (ICCST), IEEE (2014)Google Scholar
  16. 16.
    Aggarwal, C.C.: Outlier Analysis. Data Mining. Springer International Publishing (2015)Google Scholar
  17. 17.
    Akhilomen, J.: Data mining application for cyber credit-card fraud detection system. In: Proceedings of the World Congress on Engineering 2013, vol. III, 3–5 July 2013Google Scholar
  18. 18.
    Robinson, W.N., Aria, A.: Sequential fraud detection for prepaid cards using hidden Markov model divergence. Expert Syst. Appl. 91, 235–251 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Mukesh Patel School of Technology Management and Engineering (Mumbai Campus)SVKM’s NMIMS UniversityMumbaiIndia

Personalised recommendations