Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization

  • Fabrizio CarcilloEmail author
  • Yann-Aël Le Borgne
  • Olivier Caelen
  • Gianluca Bontempi


Credit card fraud detection is a very challenging problem because of the specific nature of transaction data and the labeling process. The transaction data are peculiar because they are obtained in a streaming fashion, and they are strongly imbalanced and prone to non-stationarity. The labeling is the outcome of an active learning process, as every day human investigators contact only a small number of cardholders (associated with the riskiest transactions) and obtain the class (fraud or genuine) of the related transactions. An adequate selection of the set of cardholders is therefore crucial for an efficient fraud detection process. In this paper, we present a number of active learning strategies and we investigate their fraud detection accuracies. We compare different criteria (supervised, semi-supervised and unsupervised) to query unlabeled transactions. Finally, we highlight the existence of an exploitation/exploration trade-off for active learning in the context of fraud detection, which has so far been overlooked in the literature.


Active learning Fraud detection Selection bias Semi-supervised learning 



The authors FC, YLB and GB acknowledge the funding of the Brufence project (Scalable machine learning for automating defense system) supported by INNOVIRIS (Brussels Institute for the encouragement of scientific research and innovation).


Computational resources have been provided by the Consortium des quipements de Calcul Intensif (CCI), funded by the Fonds de la Recherche Scientifique de Belgique (F.R.S.-FNRS) under Grant No. 2.5020.11.

Compliance with ethical standards

Conflicts of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.


  1. 1.
    Aggarwal, C.C.: Outlier analysis. In: Data Mining, pp. 237–263. Springer, New York (2015)Google Scholar
  2. 2.
    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
  3. 3.
    Bolton, R.J., Hand, D.J., et al.: Unsupervised profiling methods for fraud detection. In: Credit Scoring and Credit Control, vol. VII, pp. 235–255 (2001)Google Scholar
  4. 4.
    Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: Lof: identifying density-based local outliers. In: ACM Sigmod Record, vol. 29, pp. 93–104. ACM (2000)Google Scholar
  5. 5.
    Carcillo, F., Dal Pozzolo, A., Le Borgne, Y.A., Caelen, O., Mazzer, Y., Bontempi, G.: Scarff: a scalable framework for streaming credit card fraud detection with spark. Inf. Fus. 41, 182–194 (2018)Google Scholar
  6. 6.
    Carcillo, F., Le Borgne, Y.A., Caelen, O., Bontempi, G.: An assessment of streaming active learning strategies for real-life credit card fraud detection. In: DSAA-The 4th IEEE International Conference on Data Science and Advanced Analytics, vol. 7, pp. 783–790 (2017)Google Scholar
  7. 7.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 15 (2009)CrossRefGoogle Scholar
  8. 8.
    Chapelle, O., Scholkopf, B., Zien, A.: Semi-supervised learning (Chapelle, o. et al., eds.; 2006) [book reviews]. IEEE Trans. Neural Netw. 20(3), 542 (2009)CrossRefGoogle Scholar
  9. 9.
    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)zbMATHGoogle Scholar
  10. 10.
    Chen, C., Liaw, A., Breiman, L.: Using Random Forest to Learn Imbalanced Data, vol. 110. University of California, Berkeley (2004)Google Scholar
  11. 11.
    Cohn, D., Atlas, L., Ladner, R.: Improving generalization with active learning. Mach. Learn. 15(2), 201–221 (1994)Google Scholar
  12. 12.
    Dal Pozzolo, A., Caelen, O., Le Borgne, Y.A., 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
  13. 13.
    Dal Pozzolo, A., Boracchi, G., Caelen, O., Alippi, C., Bontempi, G.: Credit card fraud detection: a realistic modeling and a novel learning strategy. IEEE Trans. Neural Netw. Learn. Syst. PP(99), 1–14 (2017).
  14. 14.
    Dasgupta, S.: Two faces of active learning. Theoret. Comput. Sci. 412(19), 1767–1781 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Dorronsoro, J.R., Ginel, F., Sgnchez, C., Cruz, C.: Neural fraud detection in credit card operations. IEEE Trans. Neural Netw. 8(4), 827–834 (1997)CrossRefGoogle Scholar
  16. 16.
    Drews, P., Núñez, P., Rocha, R.P., Campos, M., Dias, J.: Novelty detection and segmentation based on gaussian mixture models: a case study in 3d robotic laser mapping. Robot. Auton. Syst. 61(12), 1696–1709 (2013)CrossRefGoogle Scholar
  17. 17.
    Ertekin, S., Huang, J., Bottou, L., Giles, L.: Learning on the border: active learning in imbalanced data classification. In: Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management, pp. 127–136. ACM (2007)Google Scholar
  18. 18.
    Fan, W., Huang, Y.A., Wang, H., Yu, P.S.: Active mining of data streams. In: Proceedings of the 2004 SIAM International Conference on Data Mining, pp. 457–461. SIAM (2004)Google Scholar
  19. 19.
    Ilonen, J., Paalanen, P., Kamarainen, J.K., Kalviainen, H.: Gaussian mixture pdf in one-class classification: computing and utilizing confidence values. In: 18th International Conference on Pattern Recognition, ICPR 2006, vol. 2, pp. 577–580. IEEE (2006)Google Scholar
  20. 20.
    Jacobusse, G., Veenman, C.: On selection bias with imbalanced classes. In: International Conference on Discovery Science, pp. 325–340. Springer, New York (2016)Google Scholar
  21. 21.
    Japkowicz, N., Stephen, S.: The class imbalance problem: a systematic study. Intell. Data Anal. 6(5), 429–449 (2002)zbMATHGoogle Scholar
  22. 22.
    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. (2018)Google Scholar
  23. 23.
    Kriegel, H.P., Kröger, P., Schubert, E., Zimek, A.: Loop: local outlier probabilities. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 1649–1652. ACM, New York, NY, USA (2009).
  24. 24.
    Lewis, D.D., Catlett, J.: Heterogeneous uncertainty sampling for supervised learning. In: In Proceedings of the Eleventh International Conference on Machine Learning, pp. 148–156. Morgan Kaufmann (1994)Google Scholar
  25. 25.
    Lewis, D.D., Gale, W.A.: A sequential algorithm for training text classifiers. In: Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 3–12. Springer, New York (1994)Google Scholar
  26. 26.
    Li, L., Hansman, R.J., Palacios, R., Welsch, R.: Anomaly detection via a Gaussian mixture model for flight operation and safety monitoring. Transp. Res. Part C Emerg. Technol. 64, 45–57 (2016)CrossRefGoogle Scholar
  27. 27.
    Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: Eighth IEEE International Conference on Data Mining. ICDM’08, pp. 413–422. IEEE (2008)Google Scholar
  28. 28.
    Palau, C., Arregui, F., Carlos, M.: Burst detection in water networks using principal component analysis. J. Water Resour. Plan. Manag. 138(1), 47–54 (2011)CrossRefGoogle Scholar
  29. 29.
    Pang, G., Cao, L., Chen, L., Liu, H.: Learning homophily couplings from non-iid data for joint feature selection and noise-resilient outlier detection. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 2585–2591. AAAI Press (2017)Google Scholar
  30. 30.
    Pang, G., Cao, L., Chen, L., Liu, H.: Unsupervised feature selection for outlier detection by modelling hierarchical value-feature couplings. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 410–419. IEEE (2016)Google Scholar
  31. 31.
    Pichara, K., Soto, A., Araneda, A.: Detection of anomalies in large datasets using an active learning scheme based on dirichlet distributions. In: Ibero-American Conference on Artificial Intelligence, pp. 163–172. Springer (2008)Google Scholar
  32. 32.
    Pimentel, M.A., Clifton, D.A., Clifton, L., Tarassenko, L.: A review of novelty detection. Signal Process. 99, 215–249 (2014)CrossRefGoogle Scholar
  33. 33.
    Pinto da Costa, J.F., Alonso, H., Roque, L.: A weighted principal component analysis and its application to gene expression data. IEEE/ACM Trans. Comput. Biol. Bioinform. 8(1), 246–252 (2011)CrossRefGoogle Scholar
  34. 34.
    Ren, D., Wang, B., Perrizo, W.: Rdf: a density-based outlier detection method using vertical data representation. In: Fourth IEEE International Conference on Data Mining, ICDM’04, pp. 503–506. IEEE (2004)Google Scholar
  35. 35.
    Rokach, L.: Decision forest: twenty years of research. Inf. Fus. 27, 111–125 (2016)CrossRefGoogle Scholar
  36. 36.
    Sahin, Y., Bulkan, S., Duman, E.: A cost-sensitive decision tree approach for fraud detection. Expert Syst. Appl. 40(15), 5916–5923 (2013)CrossRefGoogle Scholar
  37. 37.
    Schohn, G., Cohn, D.: Less is more: active learning with support vector machines. In: ICML, pp. 839–846. Citeseer (2000)Google Scholar
  38. 38.
    Schölkopf, B., Williamson, R.C., Smola, A.J., Shawe-Taylor, J., Platt, J.C.: Support vector method for novelty detection. In: Advances in Neural Information Processing Systems, pp. 582–588 (2000)Google Scholar
  39. 39.
    Seeja, KR., Zareapoor, M.: Fraudminer: a novel credit card fraud detection model based on frequent itemset mining. Sci. World J. 2014, 1–10 (2014)Google Scholar
  40. 40.
    Sethi, N., Gera, A.: A revived survey of various credit card fraud detection techniques. Int. J. Comput. Sci. Mobile Comput. 3(4), 780–791 (2014)Google Scholar
  41. 41.
    Settles, B., Craven, M., Ray, S.: Multiple-instance active learning. In: Advances in Neural Information Processing Systems, pp. 1289–1296 (2008)Google Scholar
  42. 42.
    Settles, B.: Active learning literature survey. Univ. Wis. Madison 52(55–66), 11 (2010)Google Scholar
  43. 43.
    Shimpi, P.R., Kadroli, V.: Survey on credit card fraud detection techniques. Int. J. Eng. Comput. Sci. 4(11), 15010–15015 (2015)Google Scholar
  44. 44.
    Shyu, M.L., Chen, S.C., Sarinnapakorn, K., Chang, L.: A novel anomaly detection scheme based on principal component classifier. Technical report, Miami Univ Coral Gables FL Dept of Electrical and Computer Engineering (2003)Google Scholar
  45. 45.
    Srivastava, A., Kundu, A., Sural, S., Majumdar, A.: Credit card fraud detection using hidden Markov model. IEEE Trans. Dependable Secur. Comput. 5(1), 37–48 (2008)CrossRefGoogle Scholar
  46. 46.
    Tang, J., Chen, Z., Fu, A., Cheung, D.: Enhancing effectiveness of outlier detections for low density patterns. Adv. Knowl. Discov. Data Min. 535–548 (2002)Google Scholar
  47. 47.
    Van Vlasselaer, V., Bravo, C., Caelen, O., Eliassi-Rad, T., Akoglu, L., Snoeck, M., Baesens, B.: Apate: a novel approach for automated credit card transaction fraud detection using network-based extensions. Decis. Support Syst. 75, 38–48 (2015)CrossRefGoogle Scholar
  48. 48.
    Van Vlasselaer, V., Eliassi-Rad, T., Akoglu, L., Snoeck, M., Baesens, B.: Afraid: fraud detection via active inference in time-evolving social networks. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 659–666. IEEE (2015)Google Scholar
  49. 49.
    Vijayanarasimhan, S., Jain, P., Grauman, K.: Far-sighted active learning on a budget for image and video recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3035–3042. IEEE (2010)Google Scholar
  50. 50.
    Wang, W., Guan, X., Zhang, X.: A novel intrusion detection method based on principle component analysis in computer security. Adv. Neural Netw. ISNN 2004, 88–89 (2004)Google Scholar
  51. 51.
    Wei, W., Li, J., Cao, L., Ou, Y., Chen, J.: Effective detection of sophisticated online banking fraud on extremely imbalanced data. World Wide Web 16(4), 449–475 (2013)CrossRefGoogle Scholar
  52. 52.
    Xie, J., Xiong, T.: Stochastic semi-supervised learning on partially labeled imbalanced data. In: Active Learning and Experimental Design workshop In conjunction with AISTATS 2010, pp. 85–98 (2011)Google Scholar
  53. 53.
    Zareapoor, M., Shamsolmoali, P.: Application of credit card fraud detection: Based on bagging ensemble classifier. Proc. Comput. Sci. 48, 679–685 (2015)CrossRefGoogle Scholar
  54. 54.
    Zhang, Y., Bingham, C., Martínez-García, M., Cox, D.: Detection of emerging faults on industrial gas turbines using extended Gaussian mixture models. Int. J. Rotating Mach. 2017, 1–9 (2017)Google Scholar
  55. 55.
    Zhang, K., Hutter, M., Jin, H.: A new local distance-based outlier detection approach for scattered real-world data. Adv. Knowl. Discov. Data Min. 5476, 813–822 (2009)Google Scholar
  56. 56.
    Zhu, J., Hovy, E.H.: Active learning for word sense disambiguation with methods for addressing the class imbalance problem. EMNLP-CoNLL 7, 783–790 (2007)Google Scholar
  57. 57.
    Žliobaite, I., Bifet, A., Pfahringer, B., Holmes, G.: Active learning with evolving streaming data. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 597–612. Springer (2011)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature, corrected publication April 2018 2018

Authors and Affiliations

  • Fabrizio Carcillo
    • 1
    Email author
  • Yann-Aël Le Borgne
    • 1
  • Olivier Caelen
    • 2
  • Gianluca Bontempi
    • 1
  1. 1.Machine Learning Group, Computer Science Department, Faculty of SciencesUniversité Libre de Bruxelles (ULB)BrusselsBelgium
  2. 2.R&D, WorldlineBrusselsBelgium

Personalised recommendations