Improving Card Fraud Detection Through Suspicious Pattern Discovery

  • Fabian BraunEmail author
  • Olivier Caelen
  • Evgueni N. Smirnov
  • Steven Kelk
  • Bertrand Lebichot
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10351)


We propose a new approach to detect credit card fraud based on suspicious payment patterns. According to our hypothesis fraudsters use stolen credit card data at specific, recurring sets of shops. We exploit this behavior to identify fraudulent transactions. In a first step we show how suspicious patterns can be identified from known compromised cards. The transactions between cards and shops can be represented as a bipartite graph. We are interested in finding fully connected subgraphs containing mostly compromised cards, because such bicliques reveal suspicious payment patterns. Then we define new attributes which capture the suspiciousness of a transaction indicated by known suspicious patterns. Eventually a non-linear classifier is used to assess the predictive power gained through those new features. The new attributes lead to a significant performance improvement compared to state-of-the-art aggregated transaction features. Our results are verified on real transaction data provided by our industrial partner (Worldline


Credit card fraud detection Supervised learning Feature engineering Frequent pattern mining Bicliques Graph analysis 


  1. 1.
    Aggarwal, C.C., Han, J.: Frequent Pattern Mining. Springer, Heidelberg (2014)CrossRefzbMATHGoogle Scholar
  2. 2.
    Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: International Conference on Management of Data (SIGMOD 1993), pp. 207–216. ACM, New York (1993)Google Scholar
  3. 3.
    Bhattacharyya, S., Jha, S., Tharakunnel, K., Westland, J.C.: Data mining for credit card fraud: a comparative study. Decis. Support Syst. 50(3), 602–613 (2011)CrossRefGoogle Scholar
  4. 4.
    Bolton, R.J., Hand, D.J.: Statistical fraud detection: a review. Stat. Sci. 17(3), 235–249 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Dal Pozzolo, A.: Adaptive machine learning for credit card fraud detection. Ph.D. thesis, Université libre de Bruxelles (2015)Google Scholar
  6. 6.
    Pozzolo, A.D., Caelen, O., Borgne, Y.L., Waterschoot, S., Bontempi, G.: Learned lessons in credit card fraud detection from a practitioner perspective. Expert Syst. Appl. 41(10), 4915–4928 (2014)CrossRefGoogle Scholar
  7. 7.
    Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: 23rd International Conference on Machine Learning (ICML 2006), pp. 233–240. ACM, New York (2006)Google Scholar
  8. 8.
    Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)MathSciNetzbMATHGoogle Scholar
  9. 9.
    Li, J., Liu, G., Li, H., Wong, L.: Maximal biclique subgraphs and closed pattern pairs of the adjacency matrix: a one-to-one correspondence and mining algorithms. IEEE Trans. Knowl. Data Eng. 19(12), 1625–1637 (2007)CrossRefGoogle Scholar
  10. 10.
    Liaw, A., Wiener, M.: Classification and regression by randomforest. R News 2(3), 18–22 (2002)Google Scholar
  11. 11.
    Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets. Cambridge University Press, New York (2011)CrossRefGoogle Scholar
  12. 12.
    Sánchez, D., Vila, M., Cerda, L., Serrano, J.: Association rules applied to credit card fraud detection. Expert Syst. Appl. 36(2), 3630–3640 (2009)CrossRefGoogle Scholar
  13. 13.
    Shen, A., Tong, R., Deng, Y.: Application of classification models on credit card fraud detection. In: International Conference on Service Systems and Service Management (ICSSSM 2007), pp. 1–4. IEEE (2007)Google Scholar
  14. 14.
    Spackman, K.A.: Signal detection theory: valuable tools for evaluating inductive learning. In: 6th International Workshop on Machine Learning, pp. 160–163. Morgan Kaufmann, San Francisco (1989)Google Scholar
  15. 15.
    Van Hulse, J., Khoshgoftaar, T.M., Napolitano, A.: Experimental perspectives on learning from imbalanced data. In: 24th International Conference on Machine Learning (ICML 2007), pp. 935–942. ACM, New York (2007)Google Scholar
  16. 16.
    Van Vlasselaer, V., Akoglu, L., Eliassi-Rad, T., Snoeck, M., Baesens, B.: Guilt-by-constellation: fraud detection by suspicious clique memberships. In: 48th Hawaii International Conference on System Sciences (HICSS 2015), pp. 918–927. IEEE (2015)Google Scholar
  17. 17.
    Whitrow, C., Hand, D.J., Juszczak, P., Weston, D., Adams, N.M.: Transaction aggregation as a strategy for credit card fraud detection. Data Min. Knowl. Discov. 18(1), 30–55 (2009)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Fabian Braun
    • 1
    Email author
  • Olivier Caelen
    • 2
  • Evgueni N. Smirnov
    • 3
  • Steven Kelk
    • 3
  • Bertrand Lebichot
    • 4
  1. 1.R&D, Worldline GmbHAachenGermany
  2. 2.R&D, Worldline SABrusselsBelgium
  3. 3.Department of Data Science and Knowledge EngineeringMaastricht UniversityMaastrichtThe Netherlands
  4. 4.Université catholique de LouvainLouvain-la-neuveBelgium

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