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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
Anomaly Detection dashboards and transaction anomalies tables are available for better transaction monitoring and fraud prevention.
As the database grows bigger, the number of patterns identified also increases, leading to more accurate insights.
Transaction Anomaly Detection software in the cloud. The secure, continuously evolving solution delivers faster compliance and time to market.
Seamlessly integrates with existing systems, be it a data warehouse, or a sophisticated BI system via APIs.