Big Data Concept

  Introduction Financial institutions handle vast amounts of data daily, from transactions to customer profiles, creating a complex landscape prone to fraudulent activities. Traditional rule-based systems for fraud detection often struggle to keep pace with evolving fraud tactics, especially in big data environments. Agentic AI, a new paradigm in artificial intelligence, offers a transformative approach by enabling autonomous, adaptive, and context-aware systems to detect and prevent fraud in real time. This chapter explores the role of Agentic AI in revolutionizing fraud detection within financial big data systems, delving into its mechanisms, applications, benefits, and challenges. Understanding Agentic AI Agentic AI refers to intelligent systems capable of autonomous decision-making, learning, and adaptation in dynamic environments. Unlike traditional AI, which relies heavily on predefined rules or supervised learning, Agentic AI systems operate as independent agents. They pe...

Introduction In the digital economy of 2025, financial transactions occur at an unprecedented scale, with billions processed daily through online banking, e-commerce, and mobile payments. This surge, fueled by big data, presents opportunities for efficiency but also amplifies fraud risks. Fraudulent activities, such as credit card scams, identity theft, and money laundering, cost the global economy trillions annually. Machine learning (ML), integrated with big data analytics, has become a frontline defense, enabling the identification of anomalous patterns in vast datasets that traditional rule-based systems miss. This chapter explores how big data analytics and ML revolutionize fraud detection in financial transactions. We cover foundational concepts, key algorithms, real-world applications, challenges, and future trends. By leveraging technologies like Hadoop, Spark, and advanced ML models, financial institutions can detect fraud in real-time, minimizing losses and enhancing tru...