Unsupervised Profiling Methods for Fraud Detection

By Richard J. Bolton, David J. Hand. 2002

471 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5b640c367ae9cc4bd072006b05a3ed7c2d5f496d

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Citations in the corpus

Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy (2018, background): “Both supervised [8], [12], [15] and unsupervised [11], [14], [62] methods have been proposed for credit card frauddetection purposes.”
Survey of fraud detection techniques (2004, unknown): “An advantage of using unsupervised methods over supervised methods is that previously undiscovered types of fraud may be detected.”
Deep learning detecting fraud in credit card transactions (2018, methodology): “A 10% deviation was used because it has been found to be statistically significant in maximizing classification accuracy [26]-[29].”
Random forest for credit card fraud detection (2018, unknown): “[3].”
Data mining for credit card fraud: A comparative study (2011, methodology): “Over the years, along with the evolution of fraud detection methods, perpetrators of fraud have also been evolving their fraud practices to avoid detection [3].”
Credit card fraud detection using machine learning techniques: A comparative analysis (2017, methodology): “Statistical fraud detection methods have been divided into two broad categories: supervised and unsupervised [22].”
Transaction aggregation as a strategy for credit card fraud detection (2009, background): “Also see Bolton and Hand (2002) and Provost (2002) for a discussion of the problems particular to fraud detection which statistical techniques should seek to address.”
White Rose Consortium ePrints Repository (unknown): “Bolton & Hand (Bolton and Hand, 2001) use a k-medoids type approach they call Peer Group Analysis for fraud detection.”
Intelligent financial fraud detection: A comprehensive review (2016, unknown): “In 2001 and 2002 Bolton and Hand performed some general analysis on fraud detection, focussing specifically on statistical learning [8], [9], and Rezaee investigated financial statement fraud in depth [39].”
A Comprehensive Survey of Data Mining-based Fraud Detection Research (2010, background): “Bolton and Hand (2001) also suggest Break Point Analysis to monitor intra-account behaviour over time.”
Adaptive Machine Learning for Credit Card Fraud Detection (2015, methodology): “Examples of these are given by Fawcett and Provost [183], Bolton [11] and Weston [184].”
Application of Credit Card Fraud Detection: Based on Bagging Ensemble Classifier (2015, methodology): “Accuracy is not a suitable metrics for credit card fraud detection technique since; the dataset is highly imbalanced [3].”
Fraud detection system: A survey (2016, background): “Credit card fraud detection is one of the most explored domains of fraud detection (Brause et al., 1999; Bolton and Hand, 2001; Sherly and Nedunchezhian, 2010).”
A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data (2016, background): “In particular, financial transactions can be analyzed in order to detect fraudulent accounting [6] and credit card payments logs can be used to detect misused or stolen credit cards [7].”