Real Time Credit Card Fraud Detection using Computational Intelligence

By Jon T. S. Quah, M. Sriganesh. 2007 International Joint Conference on Neural Networks, 2007

325 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/15fc43a9ed7e802e04fcbb32a2c7c88be9b9c574

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Citations in the corpus

Social big data: Recent achievements and new challenges (2015, background): “Another approach to detecting fraud in real-time credit card transactions was presented by Quah and Sriganesh [104].”
A cost-sensitive decision tree approach for fraud detection (2013, unknown): “…such as neural networks (Brause, Langsdorf, & Hepp, 1999; Dorronsoro, Ginel, Sanchez, & Cruz, 1997; Juszczak, Adams, Hand, Whitrow, & Weston, 2008; Quah & Sriganesh, 2008; Schindeler, 2006; Shen, Tong, & Deng, 2007; Stolfo, Fan, Lee, Prodromidis, & Chan, 1997; Stolfo, Fan, Lee, Prodromidis, &…”
GOTCHA! Network-Based Fraud Detection for Social Security Fraud (2017, background): “Fraud detection is a research domain with a wide variety of different applications and different requirements, including credit card fraud (Chan and Stolfo 1998, Quah and Sriganesh 2008, Unobservable Observable link link Resources (b) Figure 1 (a) Example of a spider construction.”
Fraud detection system: A survey (2016, background): “Quah and Sriganesh (2008) introduced real time credit card fraud detection using self-organization map (SOM) to decipher, ﬁ lter and analyse customer behavior for detection of fraud.”
Effective detection of sophisticated online banking fraud on extremely imbalanced data (2012, methodology): “Some supervised methods, such as Neural Network and Random Forests [10], perform well in many classification applications, including fraud detection applications, even in certain class-imbalanced scenarios [2, 9, 14, 42, 50, 67].”
Random forest for credit card fraud detection (2018, background): “Recently, a kind of fraud detection method is popular in some commercial banks which is to check behaviors of the associated cardholder [7].”
Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy (2018, methodology): “The typical cardholder’s behavior has also been modeled by means of self-organizing maps [51], [54], [71].”
Intelligent financial fraud detection: A comprehensive review (2016, unknown): “Data mining can be utilised to efficiently filter out more obvious fraud cases in the initial levels and leave the more subtle ones to be reviewed manually [37].”
Learned lessons in credit card fraud detection from a practitioner perspective (2014, methodology): “Clustering based methods (Quah & Sriganesh, 2008; Weston, Hand, Adams, Whitrow, & Juszczak, 2008) form customer profiles to identify new hidden fraud patterns.”
Credit Scoring, Statistical Techniques and Evaluation Criteria: A Review of the Literature (2011, background): “Using credit scoring can reduce the cost of the credit process and the expected risk associated with a bad loan, enhancing the credit decision, and saving time and effort (Lee et al, 2002; Ong et al, 2005).”
Credit Card Fraud Detection Using AdaBoost and Majority Voting (2019, methodology): “Loss from credit card fraud affects the merchants, where they bear all costs, including card issuer fees, charges, and administrative charges [5].”