Abstract
The problem of preprocessing transaction data for supervised fraud classification is considered. It is impractical to present an entire series of transactions to a fraud detection system, partly because of the very high dimensionality of such data but also because of the heterogeneity of the transactions. Hence, a framework for transaction aggregation is considered and its effectiveness is evaluated against transaction-level detection, using a variety of classification methods and a realistic cost-based performance measure. These methods are applied in two case studies using real data. Transaction aggregation is found to be advantageous in many but not all circumstances. Also, the length of the aggregation period has a large impact upon performance. Aggregation seems particularly effective when a random forest is used for classification. Moreover, random forests were found to perform better than other classification methods, including SVMs, logistic regression and KNN. Aggregation also has the advantage of not requiring precisely labeled data and may be more robust to the effects of population drift.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Adams NM, Hand DJ (1999) Comparing classifiers when the misallocation costs are uncertain. Pattern Recognit 32: 1139–1147. doi:10.1016/S0031-3203(98)00154-X
Aleskerov E, Freisleben B, Rao B (1997) CARDWATCH: a neural network based database mining system for credit card fraud detection. In: Computational intelligence for financial engineering. Proceedings of the IEEE/IAFE. IEEE, Piscataway, NJ, pp 220–226
APACS (2006) Fraud The Facts 2006. Retrieved May 16 2007, http://www.apacs.org.uk/resources_publications/apacs_publications_2.html
Bolton RJ, Hand DJ (2001) Unsupervised profiling methods for fraud detection. In: Conference on credit scoring and credit control, vol 7. Edinburgh
Bolton RJ, Hand DJ (2002) Statistical fraud detection: a review. Stat Sci 17: 235–249. doi:10.1214/ss/1042727940
Brause R, Langsdorf T, Hepp M (1999) Neural data mining for credit card fraud detection. In: Proceedings of the 11th IEEE international conference on tools with artificial intelligence, pp 103–106
Breiman L (2001) Random forests. Mach Learn 45: 5–32. doi:10.1023/A:1010933404324
Breiman L, Friedman JH, Ohlsen RA, Stone CJ (1984) Classification and regression trees. Wadsworth, Belmont, CA
Chan PK, Fan W, Prodromidis AL, Stolfo SJ (1999) Distributed data mining in credit card fraud detection. Intell Syst Their Appl IEEE 14: 67–74. doi:10.1109/5254.809570
Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines. Cambridge University Press, Cambridge, UK
Dorronsoro JR, Ginel F, Sanchez C, Santa Cruz C (1997) Neural fraud detection in credit card operations. IEEE Trans Neural Netw 8: 827–834. doi:10.1109/72.595879
Duda RO, Hart PE (1973) Pattern classification and scene analysis. Wiley, New York, NY, pp 10–43
Fair I (2007) FalconTM Fraud Manager web page. Retrieved June 14, 2007, http://www.fairisaac.com/fic/en/product-service/product-index/falcon-fraud-manager/
Fawcett T, Provost F (1997) Adaptive fraud detection. Data Min Knowl Discov 1(3): 291–316
Friedman JH (1989) Regularized discriminant analysis. J Am Stat Assoc 84: 165–175. doi:10.2307/2289860
Ghosh S, Reilly DL (1994) Credit card fraud detection with a neural-network. In: Nunamaker JF, Sprague RH (eds) Proceedings of the 27th annual Hawaii international conference on system science vol 3: information systems: DSS/knowledge-based systems. Los Alamitos, CA, USA
Hand DJ (2005) Good practice in retail credit scorecard assessment. J Oper Res Soc 56: 1109–1117. doi:10.1057/palgrave.jors.2601932
Hand DJ (2006) Classifier technology and the illusion of progress (with discussion). Stat Sci 21: 1–34. doi:10.1214/088342306000000060
Hand DJ, Yu K (2001) Idiot’s Bayes—not so stupid after all. Int Stat Rev 69: 385–398
Hand DJ, Whitrow C, Adams NM, Juszczak P, Weston D (2008) Performance criteria for plastic card fraud detection tools. J Oper Res Soc 59: 956–962. doi:10.1057/palgrave.jors.2602418
Hastie T, Tibshirani R, Friedman JH (2001) Elements of statistical learning. Springer
Hosmer DW, Lemeshow S (2000) Applied logistic regression. Wiley
Kelly MG, Hand DJ, Adams NM (1999) The impact of changing populations on classifier performance. In: Chaudhuri S, Madigan D (eds) Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining. Association for Computing Machinery, New York, pp 367–371
Kou Y, Chang-Tien L, Sirwongwattana S, Huang Y-P (2004) Survey of fraud detection techniques. In: IEEE international conference on networking, sensing and control, pp 749–754
Maes S, Tuyls K, Vanschoenwinkel B, Manderick B (2002) Credit card fraud detection using Bayesian and neural networks. In: Proceedings of first international NAISO congress on neuro fuzzy technologies: NF2002, Havana, Cuba. NAISO Academic Press, Canada/The Netherlands, pp 16–19
Provost F (2002) Comment on Bolton and Hand (2002). Stat Sci 17: 249–251
Provost F, Fawcett T (1997) Analysis and visualization of classifier performance: comparison under imprecise class and cost distributions. In: Proceedings of the third international conference on knowledge discovery and data mining, pp 43–48
Wheeler R, Aitken S (2000) Multiple algorithms for fraud detection. Knowl Base Syst 13: 93–99. doi:10.1016/S0950-7051(00)00050-2
Author information
Authors and Affiliations
Corresponding author
Additional information
Responsible editor: M. J. Zaki.
Rights and permissions
About this article
Cite this article
Whitrow, C., Hand, D.J., Juszczak, P. et al. Transaction aggregation as a strategy for credit card fraud detection. Data Min Knowl Disc 18, 30–55 (2009). https://doi.org/10.1007/s10618-008-0116-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10618-008-0116-z