A Frequency-domain-based Pattern Mining for Credit Card Fraud Detection

Roberto Saia, Salvatore Carta


Nowadays, the prevention of credit card fraud represents a crucial task, since almost all the operators in the E-commerce environment accept payments made through credit cards, aware of that some of them could be fraudulent. The development of approaches able to face effectively this problem represents a hard challenge due to several problems. The most important among them are the heterogeneity and the imbalanced class distribution of data, problems that lead toward a reduction of the effectiveness of the most used techniques, making it difficult to define effective models able to evaluate the new transactions. This paper proposes a new strategy able to face the aforementioned problems based on a model defined by using the Discrete Fourier Transform conversion in order to exploit frequency patterns, instead of the canonical ones, in the evaluation process. Such approach presents some advantages, since it allows us to face the imbalanced class distribution and the cold-start issues by involving only the past legitimate transactions, reducing the data heterogeneity problem thanks to the frequency-domain-based data representation, which results less influenced by the data variation. A practical implementation of the proposed approach is given by presenting an algorithm able to classify a new transaction as reliable or unreliable on the basis of the aforementioned strategy.


  1. Assis, C., Pereira, A. M., de Arruda Pereira, M., and Carrano, E. G. (2010). Using genetic programming to detect fraud in electronic transactions. In Prazeres, C. V. S., Sampaio, P. N. M., Santanchè, A., Santos, C. A. S., and Goularte, R., editors, A Comprehensive Survey of Data Mining-based Fraud Detection Research, volume abs/1009.6119, pages 337-340.
  2. Attenberg, J. and Provost, F. J. (2010). Inactive learning?: difficulties employing active learning in practice. SIGKDD Explorations, 12(2):36-41.
  3. Bolton, R. J. and Hand, D. J. (2002). Statistical fraud detection: A review. Statistical Science, pages 235-249.
  4. Brown, I. and Mues, C. (2012). An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Syst. Appl., 39(3):3446- 3453.
  5. Chatterjee, A. and Segev, A. (1991). Data manipulation in heterogeneous databases. ACM SIGMOD Record, 20(4):64-68.
  6. Donmez, P., Carbonell, J. G., and Bennett, P. N. (2007). Dual strategy active learning. In ECML, volume 4701 of Lecture Notes in Computer Science, pages 116- 127. Springer.
  7. Duhamel, P. and Vetterli, M. (1990). Fast fourier transforms: a tutorial review and a state of the art. Signal processing, 19(4):259-299.
  8. Gao, J., Fan, W., Han, J., and Yu, P. S. (2007). A general framework for mining concept-drifting data streams with skewed distributions. In Proceedings of the Seventh SIAM International Conference on Data Mining, April 26-28, 2007, Minneapolis, Minnesota, USA, pages 3-14. SIAM.
  9. He, H. and Garcia, E. A. (2009). Learning from imbalanced data. IEEE Trans. Knowl. Data Eng., 21(9):1263- 1284.
  10. Hoffman, A. J. and Tessendorf, R. E. (2005). Artificial intelligence based fraud agent to identify supply chain irregularities. In Hamza, M. H., editor, IASTED International Conference on Artificial Intelligence and Applications, part of the 23rd Multi-Conference on Applied Informatics, Innsbruck, Austria, February 14- 16, 2005, pages 743-750. IASTED/ACTA Press.
  11. Holte, R. C., Acker, L., and Porter, B. W. (1989). Concept learning and the problem of small disjuncts. In Sridharan, N. S., editor, Proceedings of the 11th International Joint Conference on Artificial Intelligence. Detroit, MI, USA, August 1989, pages 813-818. Morgan Kaufmann.
  12. Japkowicz, N. and Stephen, S. (2002). The class imbalance problem: A systematic study. Intell. Data Anal., 6(5):429-449.
  13. Lek, M., Anandarajah, B., Cerpa, N., and Jamieson, R. (2001). Data mining prototype for detecting ecommerce fraud. In Smithson, S., Gricar, J., Podlogar, M., and Avgerinou, S., editors, Proceedings of the 9th European Conference on Information Systems, Global Co-operation in the New Millennium, ECIS 2001, Bled, Slovenia, June 27-29, 2001, pages 160- 165.
  14. Lenard, M. J. and Alam, P. (2005). Application of fuzzy logic fraud detection. In Khosrow-Pour, M., editor, Encyclopedia of Information Science and Technology (5 Volumes), pages 135-139. Idea Group.
  15. Phua, C., Lee, V. C. S., Smith-Miles, K., and Gayler, R. W. (2010). A comprehensive survey of data mining-based fraud detection research. CoRR, abs/1009.6119.
  16. Pozzolo, A. D., Caelen, O., Borgne, Y. L., Waterschoot, S., and Bontempi, G. (2014). Learned lessons in credit card fraud detection from a practitioner perspective. Expert Syst. Appl., 41(10):4915-4928.
  17. Wang, H., Fan, W., Yu, P. S., and Han, J. (2003). Mining concept-drifting data streams using ensemble classifiers. In Getoor, L., Senator, T. E., Domingos, P. M., and Faloutsos, C., editors, Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 24 - 27, 2003, pages 226-235. ACM.
  18. Whiting, D. G., Hansen, J. V., McDonald, J. B., Albrecht, C. C., and Albrecht, W. S. (2012). Machine learning methods for detecting patterns of management fraud. Computational Intelligence, 28(4):505-527.

Paper Citation

in Harvard Style

Saia R. and Carta S. (2017). A Frequency-domain-based Pattern Mining for Credit Card Fraud Detection . In Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS, ISBN 978-989-758-245-5, pages 386-391. DOI: 10.5220/0006361403860391

in Bibtex Style

author={Roberto Saia and Salvatore Carta},
title={A Frequency-domain-based Pattern Mining for Credit Card Fraud Detection},
booktitle={Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,},

in EndNote Style

JO - Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,
TI - A Frequency-domain-based Pattern Mining for Credit Card Fraud Detection
SN - 978-989-758-245-5
AU - Saia R.
AU - Carta S.
PY - 2017
SP - 386
EP - 391
DO - 10.5220/0006361403860391