Learning Transactional Behavioral Representations for Credit Card Fraud Detection

Credit card fraud detection is a challenging task since fraudulent actions are hidden in massive legitimate behaviors. This work aims to learn a new representation for each transaction record based on the historical transactions of users in order to capture fraudulent patterns accurately and, thus,...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2024-04, Vol.35 (4), p.5735-5748
Hauptverfasser:	Xie, Yu, Liu, Guanjun, Yan, Chungang, Jiang, Changjun, Zhou, Mengchu, Li, Maozhen
Format:	Artikel
Sprache:	eng
Schlagworte:	Attention Behavior Behavioral sciences Credit card fraud credit card fraud detection Credit cards Datasets Detection Feature extraction Fraud Fraud prevention Learning Logic gates Long short-term memory long short-term memory (LSTM) Modules Periodicity Recurrent neural networks Representations Task analysis transactional behavioral representations
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