Enhances Security and Operational Efficiency with Forts’ Anomaly Detection Platform.

retail

Industry
Fintech

Challenge
A fintech company struggled with a high volume of false alarms in its internal anomaly detection system, leading to wasted time and delayed responses to genuine threats.

Results
By implementing Forts’ anomaly detection platform, the company reduced false positives and allow the team to focus on real anomalies and act swiftly.

Key Product
Forts - Anomaly detection.

About the company

A leading fintech company specializing in online payment solutions. With a global presence, it offers secure and seamless transactions for both consumers and businesses across various markets. Their services cater to a broad range of industries, facilitating fast and reliable cross-border payments.

The Challenge

The fintech faced challenges with its internal anomaly detection system, which produced too many false alarms. These frequent false positives overwhelmed the team, making it difficult to differentiate between real anomalies and non-critical alerts. This inefficiency wasted time and resources, delaying their ability to detect and respond to genuine threats, which could potentially expose the company to financial risk.

The Solution

The company implemented Forts’ AI-powered anomaly detection platform to address this issue. Forts’ solution leverages advanced machine learning algorithms to analyze transaction data and identify genuine anomalies more accurately. By filtering out false positives and focusing on critical alerts, the system allowed the team to concentrate on real issues. The platform’s adaptive learning capabilities ensured it continued to improve in detecting new types of anomalies over time.

The Results

With Forts’ anomaly detection platform, the company significantly reduced false alarms, streamlining operations and boosting team efficiency. This enabled faster responses to real threats, enhancing financial security and improving the overall reliability of their transaction monitoring system.

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