ResearchBib Share Your Research, Maximize Your Social Impacts
Sign for Notice Everyday Sign up >> Login

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

Journal: International Journal of Science and Research (IJSR) (Vol.11, No. 9)

Publication Date:

Authors : ;

Page : 1284-1287

Keywords : Deep Learning; Fraud Detection; Banking Security; Real - Time Analytics; Anomaly Detection; Artificial Intelligence; Financial Technology;

Source : Downloadexternal Find it from : Google Scholarexternal

Abstract

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.

Last modified: 2025-09-22 21:19:44