Credit Card Fraud Detection Using Convolutional Neural Networks

By Kang Fu, Dawei Cheng, Yi Tu, Liqing Zhang. 2016

294 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Neural Networks for Financial Market Risk Classification (2017, background): “There is already some research for a similar problem, where credit card transaction fraud detection is done by using convolutional neural networks [7].”
Deep Learning for Anomaly Detection: A Survey (2019, background): “8 Zhang et al. [2017], Castellini et al.”
Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study (2020, methodology): “The framework developed by Fu et al. [39] uses a convolutional neural network in order to capture the intrinsic patterns of fraud behaviors, especially for credit card fraud detection.”
A Systematic Review on Imbalanced Data Challenges in Machine Learning (2019, methodology): “For credit card fraud detection also, where identifying the anomalous person responsible for fraud is important task, imbalance data diminishes the accuracy of classifiers for detection (Fu et al. 2016; Kulkarni and Ade 2016).”
Learning temporal representation of transaction amount for fraudulent transaction recognition using CNN, Stacked LSTM, and CNN-LSTM (2017, methodology): “The difference between the transaction feature proposed by [18] and this study as follows.”