Real-Time Big Data Analytics for Detecting Credit Card Fraud in Cyber Forensics Using Deep Learning Models

Abstract

Real-time big data analysis and deep learning techniques for credit card fraud have been described, along with the effectiveness of a framework that has been proposed to improve the speed and accuracy of fraud detection. The framework implemented state-of-the-art technologies so that credit card transactions were monitored consistently, and dynamically developed algorithms recognized fraudulent activities. The work reflected that detection rates of deep learning models like Convolutional Neural Network (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) were higher and false positives negligible. Moreover, the analysis covered the circumstances in which the system operated in real-time interfaces and stressed that low latency and high speed in processing the many transaction records are crucial to the effective functioning of a system. The identified results highlighted the effectiveness of real-time analytics over the more conventional practices, presenting the opportunities these technologies could open for improved and more rapid fraud identification and preventing or addressing potential security threats. Specific recommendations were made concerning how financial institutions can manage big data analytics and deep learning models for fraud detection and prevention; a primary requirement was the establishment of effective data architecture, consistent training staff, etc. The implications of this research apply to cyber forensic investigators because real-time fraud detection mechanisms that stem from this research can result in more efficient identification and prosecution of fraud cases and, therefore, lower levels of loss and higher levels of security in the banking sector.