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Real-Time Fraud Detection with Deep Learning by knaveenkumarrolex is a document available to read on EtoBox.

This study explores the use of deep learning models, particularly LSTM-RNNs and CNNs, for real-time fraud detection in digital payment systems, achieving a notable 96% accuracy and an F1-score of 0.945. The findings highlight the limitations of traditional fraud detection methods and emphasize the need for advanced, adaptive mechanisms to address the complexities of modern fraud schemes. Challenges such as model interpretability, latency, and data privacy are acknowledged, suggesting future research directi

Author

knaveenkumarrolex

Language

EN