FraudLabs Pro Announces New Release of Hourly Sales Anomaly Detection for Enhanced Fraud Prevention

SARASOTA, Fla. - July 19, 2024 - PRLog -- FraudLabs Pro, a leading provider of fraud prevention solutions announced the launch of its new Hourly Sales Anomaly Detection feature. This advanced technology empowers businesses to identify and address unusual sales patterns within an hour, strengthening their defenses against fraud.

Traditional fraud detection methods often rely on daily or weekly data analysis, which can leave businesses vulnerable during periods of heightened fraudulent activity. Hourly Sales Anomaly Detection which provides real-time insights can help merchants to take immediate action and minimize potential losses before it is too late.

Key Features of FraudLabs Pro's Hourly Sales Anomaly Detection:

• Self-Learning Sales Patterns: Leveraging machine learning, the system eliminates the need for manual baseline calculations. It analyzes sales data from the past two weeks to establish a unique sales pattern for each business.
• Customizable Anomaly Detection: FraudLabs Pro offers three anomaly detection levels, allowing businesses to tailor the system's sensitivity to their specific needs. Merchants can choose to receive alerts for even minor deviations or focus on significant anomalies.
• Continuous Pattern Adaptation: The system doesn't stop learning after activation. It refreshes its understanding of sales patterns weekly using the latest two weeks of data. This ensures accurate anomaly detection even during periods of business growth.

Hourly Sales Anomaly Detection is available to FraudLabs Pro merchants who are subscribed to the Medium plan or higher. To use this new feature, just login to the FraudLabs Pro Merchant Area to enable it. For Free plan merchants, please visit https://www.fraudlabspro.com/pricing for the upgrade.

To learn more about this feature and how to enable it to secure your business, please visit https://www.fraudlabspro.com/resources/tutorials/what-is-hourly-sales-anomaly-detection/