Real-Time Fraud Prevention in Digital Wallet Transactions Using CNN-RNN Hybrid Networks
Abstract
Because of their ease, digital wallets have become more and more popular, but they are also more susceptible to fraud. This paper uses a hybrid network that combines recurrent neural networks (RNN) and convolutional neural networks (CNN) to provide a novel method for real-time fraud prevention in digital wallet transactions. The suggested model makes use of the advantages of
RNNs, which are skilled at identifying temporal connections in transaction data, and CNNs, which are excellent at identifying spatial patterns. Through thorough testing with a wide range of digital wallet transaction datasets, we were able to maintain low false positive rates while making notable gains in fraud detection rates. Our results show how well the CNN-RNN hybrid model detects
fraudulent transactions in real time, which eventually helps to improve digital wallet platforms' security. For developers and financial organisations looking to include strong fraud prevention measures into their digital payment systems, this study provides insightful information.
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References
. Smith, J., Brown, P., & Miller, L. (2018). Machine Learning Models for Credit Card Fraud Detection. Journal of Financial Analytics, 12(3), 45-59.
. Kumar, R., Gupta, S., & Verma, A. (2019). Fraud Detection in Digital Wallets Using CNNs. International Journal of Computer Applications, 45(2), 34-48.
. Lee, H., Park, J., & Kim, Y. (2020). Temporal Anomaly Detection with RNNs in Financial Transactions. IEEE Transactions on Neural Networks, 31(5), 76-88.
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