Explain how bank prevent from fruad on transactions and prevention using AI, also share latest advancement in this field explain how agentic AI playing role in fruad involvment

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Banks employ multiple layers of security to prevent fraud on transactions. Traditional methods include PINs, passwords, security questions, and basic transaction monitoring rules that flag unusual amounts or locations. Modern AI systems enhance these defenses by analyzing vast datasets to identify suspicious patterns and detect anomalies in real-time, providing much more sophisticated protection than rule-based systems alone. AI-powered fraud detection systems work by analyzing multiple data points simultaneously. These include transaction patterns, user behavior, device information, location data, and historical fraud indicators. Machine learning models process this information to calculate a risk score for each transaction in real-time. The system can identify subtle patterns that would be impossible for humans to detect, such as coordinated attacks or gradual changes in spending behavior that indicate account compromise. The latest advancements in AI for fraud prevention include several breakthrough technologies. Explainable AI provides transparency into why transactions are flagged, helping analysts understand the decision-making process. Graph Neural Networks analyze complex relationships between users, accounts, and devices to uncover sophisticated fraud rings. Real-time anomaly detection systems can now identify fraudulent activities instantly as they occur. Behavioral biometrics analyze unique user interaction patterns like typing speed and mouse movements to verify identity and detect anomalies. Agentic AI plays a double-edged role in fraud. On the defense side, autonomous AI agents can investigate suspicious alerts, automatically block high-risk transactions, verify user identities through secure channels, and adapt response strategies in real-time. However, fraudsters are also leveraging agentic AI for malicious purposes. They use it to automatically create fake identities, generate sophisticated phishing campaigns, test stolen credentials across multiple platforms at scale, and coordinate complex money laundering schemes. This creates an ongoing arms race between defensive and offensive AI capabilities. The future of AI in banking security will involve continuous evolution of machine learning models, enhanced federated learning for collaborative threat detection, quantum-resistant encryption methods, and proactive fraud prediction capabilities. Banks must maintain a competitive edge through constant innovation and industry collaboration. The AI arms race between defenders and attackers will intensify, requiring adaptive security frameworks that can evolve faster than emerging threats. Success will depend on building resilient, multi-layered defense systems that combine traditional security with cutting-edge AI technologies.