Enhancing Performance of Credit Card Model by Utilizing LSTM Networks and XGBoost Algorithms

This research paper presents novel approaches for detecting credit card risk through the utilization of Long Short-Term Memory (LSTM) networks and XGBoost algorithms. Facing the challenge of securing credit card transactions, this study explores the potential of LSTM networks for their ability to un...

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Bibliographic Details
Main Authors:	Kianeh Kandi, Antonio García-Dopico
Format:	Article
Language:	English
Published:	MDPI AG 2025-02-01
Series:	Machine Learning and Knowledge Extraction
Subjects:	recurrent neural network (RNN) long short-term memory (LSTM) network extreme gradient boosting (XGBoost) synthetic minority oversampling technique (SMOTE) imbalanced dataset
Online Access:	https://www.mdpi.com/2504-4990/7/1/20
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Enhancing Performance of Credit Card Model by Utilizing LSTM Networks and XGBoost Algorithms

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