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Anomaly detection, and more specifically fraud detection, is all about finding patterns of interest (outliers, exceptions, peculiarities, etc.) that deviate from expected behavior, and it is these systems that allow financial and insurance institutions to ensure the security of their systems.
But putting a fraud detection system in place isn’t a set-it-and-forget-it deal: it needs to constantly be evaluated and updated. With standards and systems constantly changing and under the constraint of limited resources, how can organizations ensure that data and AI systems — like a fraud detection system — stay relevant?