Predictive Analytics in Card Transactions: How AI Enhances Fraud Detection and Prevention

In particular, such ingredients as speed and convenience have become associated with the growth of fraudulent activities in the field of financial services in recent years. The risks associated with BP have been best handled by another advanced solution, known as predictive analytics, combined with artificial intelligence (AI). This paper aims to analyze the application of AI-based predictive analytics to improve fraud detection and prevention in card transactions through machine learning techniques, big data and interactive decisions. In terms of techniques, we analyze how decision trees, neural networks and Support Vector machine models work to detect fraudulent patterns. In addition, this paper reviews the deployment of these models, the assessment criteria, and the weaknesses of the proposed models used for fraud detection. With this case, we focus on assessing the effectiveness of predictive analytics in the matter of transaction safety and customer trust to provide evidence that AI may transform existing approaches to financial fraud prevention.