Credit card fraud detection and concept-drift adaptation with delayed supervised information

Proceedings of the International Joint Conference on Neural Networks

Volumn 2015-September, Issue , 2015, Pages

  Dal Pozzolo, Andrea ^a   Boracchi, Giacomo ^b   Caelen, Olivier ^c   Alippi, Cesare ^b   Bontempi, Gianluca ^a  

^a UNIVERSITÉ LIBRE DE BRUXELLES   (Belgium)

^b POLITECNICO DI MILANO   (Italy)

^c Fraud Risk Management Analytics ^*  (Belgium)

Author keywords

Anomaly Detection; Concept Drift; Data Streams; Fraud Detection; Unbalanced Data

Indexed keywords

NEURAL NETWORKS;

ANOMALY DETECTION; CONCEPT DRIFTS; DATA STREAM; FRAUD DETECTION; UNBALANCED DATA;

CRIME;

EID: 84951012186     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2015.7280527     Document Type: Conference Paper

References (38)

1. E. Aleskerov, B. Freisleben, and B. Rao. Cardwatch: A neural network based database mining system for credit card fraud detection. In Computational Intelligence for Financial Engineering (c/FEr). 1997., Proceedings of the IEEEIIAFE 1997, pages 220-226. IEEE, 1997.
2. C. Alippi, G. Boracchi, and M. Roveri. Just-in-time classifiers for recurrent concepts. Neural Networks and Learning Systems, IEEE Transactions on, 24(4):620-634, April.
3. G. Batista, A. Carvalho, and M. Monard. Applying one-sided selection to unbalanced datasets. MICAl 2000: Advances in Artificial Intelligence, pages 315-325, 2000.
4. S. Bhattacharyya, S. Jha, K. Tharakunnel, and J. C. Westland. Data mining for credit card fraud: A comparative study. Decision Support Systems, 50(3):602-613, 2011.
5. R. Bolton and D. Hand. Statistical fraud detection: A review. Statistical Science, pages 235-249, 2002.
6. R. Bolton, D. Hand, et al. Unsupervised profiling methods for fraud detection. Credit Scoring and Credit Control VII, pages 235-255, 2001.
7. R. Brause, T. Langsdorf, and M. Hepp. Neural data mining for credit card fraud detection. In Tools with Artificial Intelligence, 1999. Proceedings. 11th IEEE International Conference on, pages 103-106. IEEE, 1999.
8. L. Breiman. Stacked regressions. Machine learning, 24(1):49-64, 1996.
9. L. Breiman. Random forests. Machine learning, 45(1):5-32, 2001.
10. P. Chan, W. Fan, A. Prodromidis, and S. Stolfo. Distributed data mining in credit card fraud detection. Intelligent Systems and their Applications, 14(6):67-74, 1999.
11. S. Chen and H. He. Towards incremental learning of nonstationary imbalanced data stream: A mUltiple selectively recursive approach. Evolving Systems, 2(1):35-50, 2011.
12. A. Dal Pozzolo, O. Caelen, Y-A. Le Borgne, S. Waterschoot, and G. Bontempi. Learned lessons in credit card fraud detection from a practitioner perspective. Expert Systems with Applications, 41(10):4915-4928, 2014.
13. A. Dal Pozzolo, R. Johnson, O. Caelen, S. Waterschoot, N. Y. Chawla, and G. Bontempi. Using hddt to avoid instances propagation in unbalanced and evolving data streams. In Neural Networks (. JCNN), The 2014 International Joint Conference on. IEEE, 2014.
14. J. Demsar. Statistical comparisons of classifiers over mUltiple data sets. The Journal of Machine Learning Research, 7: 1-30, 2006.
15. G. Ditzler and R. Polikar. An ensemble based incremental learning framework for concept drift and class imbalance. In Neural Networks (. JCNN). The 2010 international Joint Co'ference on, pages 1-8. IEEE, 2010.
16. J. Dorronsoro, F. Ginel, C. Sgnchez, and C. Cruz. Neural fraud detection in credit card operations. Neural Networks, 8(4):827-834, 1997.
17. W. Fan. Systematic data selection to mine concept-drifting data streams. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 128-137. ACM, 2004.
18. M. Friedman. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, 32(200):675-701, 1937.
19. J. Gama, I. Zliobaite, A. Bifet, M. Pechenizkiy, and A. Bouchachia. A survey on concept drift adaptation. ACM Compltling Surveys (CSUR), 46(4):44, 2014.
20. J. Gao, B. Ding, W. Fan, J. Han, and P. S. Yu. Classifying data streams with skewed class distributions and concept drifts. Internet Compltling, 12(6):37-49, 2008.
21. J. Gao, W. Fan, J. Han, and S. Y Philip. A general framework for mining concept-drifting data streams with skewed distributions. In SDM, 2007.
22. S. Ghosh and D. L. Reilly. Credit card fraud detection with a neuralnetwork. In System Sciences, 1994. Proceedings of the Twenty-Seventh Hawaii International Conference on, volume 3, pages 621-630. IEEE, 1994.
23. D. Hand, C. Whitrow, N. Adams, P. Juszczak, and D. Weston. Performance criteria for plastic card fraud detection tools. Journal of the Operational Research Society, 59(7):956-962, 2008.
24. H. He and E. A. Garcia. Learning from imbalanced data. Knowledge and Data Engineering, lEEE Transactions on, 21(9): 1263-1284, 2009.
25. P. Juszczak, N. M. Adams, D. J. Hand, C. Whitrow, and D. J. Weston. Off-the-peg and bespoke classifiers for fraud detection. Computational Statistics & Data Analysis, 52(9):4521-4532, 2008.
26. J. Z. Kolter and M. A. Maloof. Dynamic weighted majority: An ensemble method for drifting concepts. The Journal of Machine Learning Research, 8:2755-2790, 2007.
27. G. Krempl and Y. Hofer. Classification in presence of drift and latency. In Data Mining Workshops (ICDMW). 2011 lEEE 11th international Co'ference on, pages 596-603. IEEE, 2011.
28. R. N. LichtenwaIter and N. Y. Chawla. Adaptive methods for classification in arbitrarily imbalanced and drifting data streams. In New Frontiers in Applied Data Mining, pages 53-75. Springer, 2010.
29. D. Malekian and M. R. Hashemi. An adaptive profile based fraud detection framework for handling concept drift. In Information Security and Cryptology (ISCISC), 2013 10th International lSC Conference on, pages 1-6. IEEE, 2013.
30. C. Phua, Y. Lee, K. Smith, and R. Gayler. A comprehensive survey of data mining-based fraud detection research. ArXiv preprint arXiv:l009. 6119, 2010.
31. J. T. Quah and M. Sriganesh. Real-time credit card fraud detection using computational intelligence. Expert Systems with Applications, 35(4):1721-1732, 2008.
32. J. C. Schlimmer and R. H. Granger Jr. Incremental learning from noisy data. Machine learning, 1(3):317-354, 1986.
33. W. N. Street and Y. Kim. A streaming ensemble algorithm (sea) for large-scale classification. In Proceedings of the seventh ACM SlGKDD international conference on Knowledge discovery and data mining, pages 377-382. ACM, 2001.
34. D. K. Tasoulis, N. M. Adams, and D. J. Hand. Unsupervised clustering in streaming data. In ICDM Workshops, pages 638-642, 2006.
35. H. Wang, W. Fan, P. S. Yu, and J. Han. Mining concept-drifting data streams using ensemble classifiers. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 226-235. ACM, 2003.
36. D. Weston, D. Hand, N. Adams, C. Whitrow, and P. Juszczak. Plastic card fraud detection using peer group analysis. Advances in Data Analysis and Classification, 2(1):45-62, 2008.
37. C. Whitrow, D. J. Hand, P. Juszczak, D. Weston, and N. M. Adams. Transaction aggregation as a strategy for credit card fraud detection. Data Mining and Knowledge Discovery, 18(1):30-55, 2009.
38. G. Widmer and M. Kubat. Learning in the presence of concept drift and hidden contexts. Machine learning, 23(1):69-101, 1996.