Leveraging Deep Learning for Enhanced Fraud Detection in Banking: A Comprehensive Analysis of Strategies and Future Directions

Fraudulent activities in the banking sector are escalating in complexity, posing significant challenges to traditional detection systems. As digital transactions become increasingly prevalent, the need for adaptive and scalable fraud detection methods has never been greater. Deep learning offers transformative potential, utilizing models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders to identify anomalies in real - time transactions. This paper provides an in - depth exploration of these models, detailing their architectures, applications, advantages, and limitations. It also addresses implementation challenges such as computational demands, data privacy concerns, and system integration, offering practical solutions and future directions. By leveraging the capabilities of deep learning, financial institutions can strengthen security, enhance customer trust, and stay ahead of evolving fraud tactics.