Case Study: Fraud Detection Through User Behavior Analytics

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In this case study, we dive into the patterns of suspicious user activity to better understand and detect potential fraud risks. By analyzing key performance indicators (KPIs) such as transaction amount, session duration, and failed login attempts, we uncover trends, pinpoint regional hotspots, and identify device-specific risks to improve monitoring strategies.