Quantum Autoencoder for Enhanced Fraud Detection in Imbalanced Credit Card Dataset

Credit card fraud detection is crucial for financial security which entails identifying unauthorized transactions that can result in significant financial losses. Detection is inherently challenging due to the rarity and indistinguishability of fraudulent transactions from genuine ones, which makes...

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Veröffentlicht in:	IEEE access Jg. 12; S. 169671 - 169682
Hauptverfasser:	Huot, Chansreynich, Heng, Sovanmonynuth, Kim, Tae-Kyung, Han, Youngsun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Anomalies Anomaly detection Computational efficiency Computational modeling Credit card fraud credit card fraud detection Credit cards Datasets Fraud Fraud prevention imbalanced dataset Integrated circuit modeling Noise quantum autoencoder (QAE) Quantum computing quantum machine learning (QML) Quantum state Real time Real-time systems
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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