Survey of fraud detection techniques

By Yufeng Kou, Chang-Tien Lu, S. Sirwongwattana, Yo-Ping Huang. IEEE International Conference on Networking, Sensing and Control, 2004

501 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/5a5b0024f2a69cee332ea2a608ec980948bb67ba

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In Artificial Intelligence

In Computer Networks and Communications

Citations in the corpus

Intelligent financial fraud detection: A comprehensive review (2016, unknown): “In 2004 Kou et al. reviewed the study of general fraud detection using analytic techniques including neural networks [30].”
k-means-: A Unified Approach to Clustering and Outlier Detection (2013, unknown): “Furthermore we extend our approach to all distance measures that can be expressed in the form of a Bregman divergence.”
Transaction aggregation as a strategy for credit card fraud detection (2009, background): “C. Whitrow (B) · D. J. Hand · P. Juszczak · D. Weston Institute for Mathematical Sciences, Imperial College, London, UK e-mail: c.whitrow@imperial.ac.uk D. J. Hand · N. M. Adams Department of Mathematics, Imperial College, London, UK”
Adaptive Machine Learning for Credit Card Fraud Detection (2015, background): “They model a baseline distribution that represents normal behavior and then attempt to detect observations that show the greatest departure from this norm [202].”
Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach (2019, methodology): “The conventional electricity theft detection method has been addressed with statistical techniques [26,27], comparing the abnormal and normal meter readings, fuzzy networks, and rough sets [28,29].”
The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature (2011, background): “[47] highlight that an important advantage of data mining is that it can be used to develop a new class of models to identify new attacks before”
Energy-theft detection issues for advanced metering infrastructure in smart grid (2014, methodology): “The detection and identification of frauds in power systems has been initially addressed with statistical techniques[25, 26].”
Credit card fraud detection: A fusion approach using Dempster-Shafer theory and Bayesian learning (2009, background): “Kou et al. [20] have reviewed the various fraud detection techniques including credit card fraud, telecommunication fraud as well as computer intrusion detection.”
Effective detection of sophisticated online banking fraud on extremely imbalanced data (2012, background): “Most of the work on preventing and detecting credit card fraud has been carried out with neural networks [36].”
Data mining for credit card fraud: A comparative study (2011, background): “A summary of these is given in [28], which reviews analytic techniques for general fraud detection, including credit card fraud.”
Fraud detection system: A survey (2016, background): “These huge number of losses have increased the importance of fraud ﬁ ghting Kou et al., 2004).”
A cost-sensitive decision tree approach for fraud detection (2013, background): “Lastly, the data sets are also constantly evolving making the profiles of normal and fraudulent behaviors always changing (Bolton & Hand, 2002; Kou et al., 2004; Phua et al., 2005; Sahin & Duman, 2010).”
On the Fintech Revolution: Interpreting the Forces of Innovation, Disruption, and Transformation in Financial Services (2018, background): “[96]) and substantial effort into improving the models, the high costs associated with missing fraudulent transactions continue to create the impetus for significant investments in developing sophisticated algorithms for detection.”
Credit card fraud detection using machine learning techniques: A comparative analysis (2017, background): “A detailed discussion of supervised and unsupervised techniques is found in [23].”