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    Transaction aggregation as a strategy for credit card fraud detection

    Whitrow, C. and Hand, D.J. and Juszczak, P. and Weston, David J. and Adams, N.M. (2009) Transaction aggregation as a strategy for credit card fraud detection. Data Mining and Knowledge Discovery 18 (1), pp. 30-55. ISSN 1384-5810.

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    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.


    Item Type: Article
    Keyword(s) / Subject(s): Fraud detection, Supervised classification, Credit cards, Preprocessing
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Depositing User: Administrator
    Date Deposited: 13 Jun 2013 14:28
    Last Modified: 09 Aug 2023 12:33


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