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Anomaly detection in financial transactions classifies data into normal distribution and outliers. When a transaction or a data point deviates from a dataset’s normal behavior, it can be considered potentially fraudulent.
If a transaction looks suspicious and potentially fraudulent, the system may ask the customer to verify details or go through additional verification steps.
ML algorithms are used to find the very subtle and usually hidden events and correlations in user behavior that may signal fraud
Enables companies to immediately respond to deviations from the norm, potentially saving millions that would have been lost to fraud otherwise
An anomaly detection dashboards and transaction anomalies table for a detailed view are available