Deep Learning Techniques for Image Recognition and Classification

Abstract

The convergence of Artificial Intelligence (AI) and Machine Learning (ML) has given rise to powerful tools that have significantly impacted various industries. One of the critical areas benefiting from this synergy is fraud prevention. Fraudulent activities have grown in complexity, and traditional mechanisms often fail to address evolving threats. AI and ML techniques enhance fraud detection systems, providing an adaptive, dynamic approach to identifying fraudulent patterns and behaviors. This research investigates the role of AI and ML in strengthening fraud prevention strategies, focusing on their applications across financial sectors, e-commerce, and cybersecurity. By examining existing frameworks, methodologies, and experiments in fraud prevention, the paper explores the potential of AI and ML to deliver more efficient, accurate, and scalable solutions. The results show how AI-driven systems, leveraging ML algorithms, provide real-time detection and mitigation, thus transforming the way fraud is prevented. The paper concludes by discussing the future of AI and ML in fraud prevention and identifying challenges and opportunities for continued innovation.