Fraud detection has become increasingly important with the increase in automated and digital transactions. IoT fraud detection systems collect and use big data in real-time to detect fraudulent financial activity, send alerts, and block transactions. Real-time big data processing combined with machine learning algorithms can be very effective in anomaly detection and the identification of previously unknown issues that may be responsible for quality problems or security threats. This enables service providers to eliminate existing anomalies and prevent future ones, as well as to detect problems more rapidly and solve them proactively. Anomalies can be detected by analyzing device behavior, network dynamics, use across groups of devices owned by one customer, or location patterns.
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