Enhancing Fraud Detection in Banking with Deep Learning Graph Neural Networks and Auto Encoders for Real-Time Credit Fraud Prevention
Y Venkata Subbaiah,
R Chaitanya Lakshmi,
N Kusuma,
S Yasin,
S Harsha Vardhan Reddy
Under the umbrella of artificial intelligence (AI), deep learning enables systems to cluster data and
provide incredibly accurate results. This study explores deep learning for fraud detection, utilizing
Graph Neural Networks (GNNs) and Autoencoders to enhance business practices and reduce fraudulent
activities in large organizations. For real-time fraud detection, we propose Graph neural network with
lambda architecture while for credit card fraud detection, we use an autoencoder, validated through
case studies from two banks. The findings demonstrate that these methods effectively detect fraud with
balance of precision and recall, improving the efficiency of banking systems. Python is employed for
analysis, emphasizing the ability of deep learning to manage and prevent fraud in real-time on dynamic
datasets. In the end, this study concludes that by using deep learning algorithms, we can control online
credit card fraud detection in banks, improve the efficiency of the banking system. We can manage
fraudulent activity in real-time and on dynamic datasets by utilizing deep learning algorithms, which
allows for ongoing improvement of the fraud detection and prevention system.
