Foresight: Advanced Algorithms for Detecting Plastic Money Fraud

The widespread adoption of plastic money, such as credit and debit cards, for online payments has made financial systems increasingly vulnerable to fraud attempts by cybercriminals. Safeguarding financial security requires effective detection and mitigation of such fraudulent transactions. Machine learning offers a powerful solution for this challenge by identifying suspicious patterns within transaction data. This paper investigates the use of various machine learning models—including Convolutional Neural Networks (CNN), Random Forest, Naïve Bayes, and Artificial Neural Networks (ANN)—to categorize transactions as either fraudulent or genuine. Furthermore, the study explores enhanced accuracy and detection capabilities achieved by combining two models in a hybrid approach. This strategy enables financial institutions to proactively detect and address fraudulent activities, strengthening overall system security. The findings highlight the significant potential of machine learning in advancing fraud detection, ultimately fostering a safer and more dependable digital financial environment.