A Comprehensive Survey of Data Mining-based Fraud Detection Research

By Clifton Phua, Vincent Cheng-Siong Lee, K. Smith‐Miles, Ross W. Gayler. 2010

953 citations recorded by the source corpus.

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Citations in the corpus

The future of employment: How susceptible are jobs to computerisation? (2017, background): “As such, this task is now almost completely automated (Phua et al., 2010).”
Deep Reinforcement Learning: An Overview (2017, methodology): “FinTech employs machine learning techniques to deal with issues like fraud detection (Phua et al., 2010), consumer credit risk (Khandani et al.”
Intelligent financial fraud detection: A comprehensive review (2016, unknown): “None of the existing reviews (for example, [32], [62] and [63]) covers this aspect.”
Computer-aided auditing of prescription drug claims (2013, background): “We direct the reader to survey papers on fraud detection, from a general perspective in [18], and from a specific health care perspective in [16].”
Spatial anomaly detection in sensor networks using neighborhood information (2017, methodology): “A typical example is the detection of fraud in, e.g., credit card transactions or the detection of identity falsi-ﬁcation [16] .”
Credit Card Fraud Detection Using Hidden Markov Model (2012, background): “Phua et al. [13] have done an extensive survey of existing data-mining-based FDSs and published a comprehensive report.”
Fraud detection system: A survey (2016, background): “In fraud detection, a false negative error is usually more costly than a false positive error (Phua et al., 2005).”
MacroBase: Analytic Monitoring for the Internet of Things (2016, background): “Anomaly detection techniques have seen major success in several vertically-oriented domains including network intrusion detection (frequently based on “fingerprint” and rules-based methods) [20, 38], various kinds of fraud detection (leveraging a variety of classifiers and techniques) [8, 39], and i”
Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy (2018, methodology): “A well-known method is peer group analysis [65], which clusters customers according to their profile and identifies frauds as transactions departing from the typical cardholder’s behavior (also see [52]).”
Graph based anomaly detection and description: a survey (2014, methodology): “Finally, due to the wide-range of application domains, fraud detection has attracted many surveys (Edge and Falcone Sampaio 2009; Flegel et al. 2010; Phua et al. 2010).”
MacroBase: Prioritizing Attention in Fast Data (2016, background): “Outlier detection techniques have seen major success in several domains including network intrusion detection [32,61], fraud detection (leveraging a variety of classifiers and techniques) [14, 64], and industrial automation and predictive maintenance [8, 56].”
Anomaly detection in online social networks (2014, methodology): “…Bichler (2011); Cheng and Dickinson (2013)), clandestine organisational structures (e.g. Shetty and Adibi (2005); Krebs (2002); Reid et al. (2005)), and fraudulent and predatory activity (e.g. Phua et al. (2010); Fire et al. (2012); Chau et al. (2006); Akoglu et al. (2013); Pandit et al. (2007)).”
The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature (2011, methodology): “[58] describe fraud as leading to the abuse of a profit organization's systemwithout necessarily leading to direct legal consequences.”
Corporate residence fraud detection (2014, background): “A comprehensive overview of the complete fraud detection literature is beyond the scope of this paper (see [4, 26, 29]).”