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Fraud detection has evolved significantly with the advent of AI and machine learning (ML), transitioning from static rule-based systems to dynamic, data-driven models. ML excels in real-time analysis, pattern recognition, scalability, and automation, making it a powerful tool against sophisticated fraud tactics. Its applications span banking, e-commerce, insurance, and cybersecurity, utilizing techniques like supervised, unsupervised, and reinforcement learning.
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