ADF: Find suspicious events, actions, and behavior in your data. Detect problems in seconds instead of days, automate their inspection, and visualize them easily.
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Ability to detect anomalies in data in near real-time, with detection time reduced from days/hours to minutes/seconds
Ability to process terabytes of data in parallel, lets you scale up your monitoring processes by orders of magnitude
Acceleration and automation of issue detection, enabling faster reaction and problem prevention
Visualization and explanation of the detected anomalies, to keep decision-makers, operators, and analysts in charge
ADF warns you even before the problem happens
ADF (Anomaly Detection Framework) finds anomalies in large volumes of complex data in near real-time. It visualizes and explains the detected problem, letting you take appropriate action. The system learns to detect anomalous behaviors that exceed the statistical probability of randomness.
Helps in finance or computer networks
Anomalies in financial data typically signify possible fraud or misconduct. ADF helps you to find errors and unmatched records in financial transactions and reveal hidden risks or issues in employee performance in attendance reports. ADF also wades through streams of data from various systems in your computer network, detecting novel cybersecurity threats or malware, and problems in the performance of critical devices, or applications.
ADF used in production, manufacturing, and marketing
ADF improves the quality of products and uptime of production lines, analyzing data from sensors on production lines, looking for early warnings leading to a potential malfunction. It also helps you with product-quality management, by inspecting manufactured product properties in real-time or comparing final product test data against manufacturing data and pinpointing inefficiencies. In digital marketing, ADF spots anomalies in bounce rate, social media sentiment, and other important metrics, letting you improve performance.
Increased fraud detection efficiency
ČSOB Pojišťovna, part of KBC Group and the 5th-largest insurer in the Czech Republic, innovated its fraud-detection process to increase fraud-detection efficiency, decrease false positives, and protect itself from evolving fraud activity. ADF, when applied to historical claims data, automated the fraud-detection process, allowing analysts to concentrate on relevant cases, reducing false positives by 60%, increasing operational efficiency, and improving customer experience.