Real-time computational intelligence model for credit card fraud detection in cyber forensics

This paper is aimed at developing a computational intelligence model for real-time detection and prevention of credit card fraudulent transactions within digital and cyber forensic investigations. Decision Trees, Support Vector Machines and Artificial Neural Networks were employed in the design of the system to ensure reliable and efficient fraud detection. In order to eliminate noise and enhance the accuracy of the analysis, the actual transaction data entered in the data set was used. The model was trained through supervised learning technique to identify fraudulent patterns in real time. To verify the effectiveness of the developed system, post-hoc comparisons were done regarding the models in terms of accuracy, precision, recall, and f1 score. The calculation revealed that the Artificial Neural Networks provide the best accuracy for the detection of fraud as it reached 98% precision for correct fraudulent activity identification. The research has helped to reduce the rise in credit card fraud within the digital ecosystem by employing contemporary computational approaches. It also assists cyber forensic investigators to mitigate financial damage and enhances security measures in financial institutions.