A NEW PARADIGM IN FRAUD DETECTION: LEVERAGING SOCIAL MEDIA TO PREDICT MCCS IN REAL TIME

Card-not-present (CNP) fraud presents a persistent challenge in the financial industry, costing institutions billions annually. Traditional fraud detection models rely heavily on transaction history and rule-based mechanisms, making them reactive rather than proactive. These systems struggle to detect emerging fraud patterns that evolve beyond historical transaction-based limitations. In this study, we introduce an innovative fraud detection framework that leverages social media analytics to predict a cardholder’s likely future Merchant Category Code (MCC), providing a proactive layer of fraud prevention. Our approach integrates natural language processing (NLP) techniques with machine learning to analyze publicly available social media posts from business pages linked to specific MCC categories. By transforming unstructured text data using Term Frequency-Inverse Document Frequency (TF-IDF) vectorization, we extract relevant linguistic features and apply a Multinomial Naïve Bayes classifier to predict MCCs with high accuracy. To ensure model scalability and robustness, we expanded our dataset from 120,000 to over three million records using data amplification techniques. Our model achieves an accuracy of 99.1% through cross-validation, significantly improving real-time MCC prediction for transaction authentication. Unlike conventional fraud detection systems that react only to anomalies in past spending behavior, our method proactively forecasts spending categories based on dynamic social media interactions. By comparing the predicted MCCs with those in real-time transactions, financial institutions can identify potential fraudulent activities before they occur. This novel integration of behavioral insights from social media into fraud detection enhances the adaptability of fraud prevention systems, bridging the gap between static transaction monitoring and real-time fraud anticipation. This research demonstrates that incorporating social media discourse into financial security mechanisms can revolutionize CNP fraud detection. Future work will focus on expanding the model to accommodate multilingual data and additional MCC categories, ensuring its applicability across diverse consumer markets.