Transaction aggregation as a strategy for credit card fraud detection

By Christopher Whitrow, David J. Hand, Piotr Juszczak, David John Weston, Niall M. Adams. Data mining and knowledge discovery, 2009

303 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/2f628faea691487c44acb68753142ded17d42012

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Citations in the corpus

A Semi-Supervised Graph Attentive Network for Financial Fraud Detection (2019, background): “Therefore, a great number of literatures have been studied on different types of the fraud, like financial statement fraud [7]– [9], credit card fraud [10], [11], insurance fraud [12], [13]”
APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions (2015, background): “If the transaction clears the acceptance checks, it is passed on to the sanity check of the detection system.”
Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy (2018, methodology): “Several studies have reported random forest (RF) to achieve the best performance [8], [20], [23], [63], [66]: this is one of the reasons why we adopt RFs in our experiments.”
Mining data with random forests: A survey and results of new tests (2011, methodology): “QDA, CART, NB) [49] Data: two data sets fro”
Corporate residence fraud detection (2014, background): “Other studies [3, 19, 39], take the approach of deriving RFM (Recency, Frequency, Monetary Value) attributes from the original features over a period of time.”
Sequence classification for credit-card fraud detection (2018, background): “[Whitrow et al., 2009] proposed feature aggregation strategies to summarize the historic purchase activities of card holders.”
Feature engineering strategies for credit card fraud detection (2016, background): “…a customer transactions, there is an imporant question on how much to accumulate, in the sense that the arginal value of new information may diminish as time passes. hitrow et al. (2008) discuss that aggregating 101 transactions is ot likely to be more informative than aggregating 100 transacions.”
Adaptive Machine Learning for Credit Card Fraud Detection (2015, methodology): “Finally, between all the algorithms proposed in the literature, we recommend using Random Forest, because several studies have shown that it achieves the best results among different classifiers [18, 20, 61, 63, 64, 71].”
Learned lessons in credit card fraud detection from a practitioner perspective (2014, unknown): “All the results are obtained by experimentation on a dataset of real credit card transactions provided by our industrial partner.”
Data mining for credit card fraud: A comparative study (2011, methodology): “A recent paper [49] evaluates several techniques, including support vector machines and random forests for predicting credit card fraud.”
Intelligent financial fraud detection: A comprehensive review (2016, unknown): “Whitrow et al. compared support vector machines with decision trees in solving credit card fraud, with a focus on aggregating common transactional variables to create new inputs [50].”