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Behavioral Intelligence Platform

Modern fraud demands the adaptive and predictive capabilities of behavioral intelligence.

ThreatMark’s Behavioral Intelligence platform helps detect fraud in real-time and prevent the fraudsters from successfully completing their attack.

Revolutionizing fraud prevention.

The ThreatMark Behavioral Intelligence platform is a sophisticated technology system that analyzes and interprets the behaviors and actions of users to identify potential fraud. Leveraging data analytics, machine learning algorithms, and artificial intelligence to monitor and evaluate, ThreatMark focuses on how users interact with digital banking systems in real-time.

Behavioral intelligence starts the process of protecting a financial institution’s customer early in the digital journey. The platform gathers and analyzes data on user behavior patterns—ranging from login frequencies and transaction habits to navigation preferences and keystroke dynamics—to establish a normative profile for each user. This continuous monitoring enables the detection of deviations that may signify fraudulent activity, such as unexpected login locations or unusual transaction patterns, allowing for immediate action to prevent potential fraud.

ThreatMark excels at identifying known fraud schemes, adapting to new patterns of conduct and enhancing their efficacy over time. This capability ensures a nuanced response to detected anomalies, categorizing them based on risk levels and thereby streamlining the response protocol based on the financial institution’s requirements. The platform also helps minimizing false positives which improves the user experience, ensuring legitimate transactions proceed unhindered.

Mitigating sophisticated fraud.

  • Real-Time Monitoring

    ThreatMark operates in real time, providing immediate feedback when suspicious behavior is detected. This allows financial institutions to quickly respond to potential threats before they result in significant losses.

  • Behavior Analysis

    ThreatMark produces a profile on how users typically interact with the digital platform, including login patterns, transaction behaviors, and navigation paths, creating a behavioral profile or baseline for each user.

  • Anomaly Detection

    By continuously monitoring the user’s digital journey, ThreatMark can detect deviations from normal behavior patterns that might indicate potential fraudulent activities.

  • Machine Learning and AI

    The use of machine learning and AI enables ThreatMark to not only detect known fraud patterns but also learn from new behaviors and adapt over time, becoming effective at identifying fraud as it is exposed to more data.

  • Risk Analysis

    ThreatMark can assess the risk level of different actions, allowing for a nuanced response. For example, a low-risk anomaly may result in additional authentication steps, whereas high-risk behavior could trigger account locks or alerts to fraud teams.

  • Enhanced Customer Experience

    By accurately distinguishing between legitimate user behavior and fraudulent activity, financial institutions can reduce false positives - legitimate transactions that are incorrectly flagged as fraud - improving the customer experience.

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