Data mining for credit card fraud: A comparative study

By Siddhartha Bhattacharyya, Sanjeev Jha, Kurian K. Tharakunnel, J. Westland. Decision Support Systems, 2011

1,041 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/9d26f0ba02ee5efe9b9c7bdcb5f528c8b8253cf7

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Citations in the corpus

Unique in the shopping mall: On the reidentifiability of credit card metadata (2015, background): “At the same time, financial data sets have been used extensively for credit scoring (26), fraud detection (27), and understanding the predictability of shopping patterns (28).”
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.”
A Semi-Supervised Graph Attentive Network for Financial Fraud Detection (2019, methodology): “Commonly, these methods first extract statistical features from different aspects like user’s profiles and historical behaviours and then use some classical classifiers like SVM [9], treebased methods [10] or neural networks [7], [8] to decide fraud or not.”
Learning temporal representation of transaction amount for fraudulent transaction recognition using CNN, Stacked LSTM, and CNN-LSTM (2017, methodology): “Many methods have been proposed such as: Random forest [4] [5], Decision Tree [6], K-Nearest Neighbors [7], SVM [8] [9], HMM [10], neural networks [11], Bayesian learning [11], artificial immune systems [12], association rules [13], hybrid models [14], discriminant analysis [15].”
Explaining prediction models and individual predictions with feature contributions (2014, background): “Applications range from credit scoring [11] and fraud detection [5] to financial auditing [4] and efficiency analysis [18].”
Random forest for credit card fraud detection (2018, background): “However, single-tree model is possibly sensitive to specific training data and easy to overfit [11].”
APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions (2015, background): “While e-commerce is still gaining popularity, it is also the playground of fraudsters who try to misuse the transparency of online purchases and the transfer of credit card records.”
Sequence classification for credit-card fraud detection (2018, neutral): “[Bhattacharyya et al., 2011] compared the predictive accuracy of Logistic Regression, Random Forests and Support Vector Machines on a real-world credit-card fraud data set under varying proportions of fraud in the training set, finding that Random Forests demonstrated an overall higher precision at ”
An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine (2020, methodology): “Both supervised learning [8], and unsupervised learning [12] algorithms have been utilized for credit card fraud detection.”
Credit card fraud detection using machine learning techniques: A comparative analysis (2017, methodology): “This paper [13] evaluates two advanced data mining approaches, support vector machines and random forests, together with logistic regression, as part of an attempt to better detect credit card fraud while neural network and logistic regression is applied on credit card fraud detection problem [14].”
Credit Card Fraud Detection Using AdaBoost and Majority Voting (2019, methodology): “A. SINGLE MODELS For credit card fraud detection, Random Forest (RF), Support Vector Machine, (SVM) and Logistic Regression (LOR) were examined in [6].”
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].”