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    This study introduces a visual analytics framework for interpreting user behaviour analytics (UBA) models. Interactive hierarchical user profiles help analysts understand user activities and make informed decisions on suspicious behaviour.

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    Area of Science:

    • Computer Science
    • Human-Computer Interaction
    • Data Visualization

    Background:

    • User behaviour analytics (UBA) systems identify fraudulent activities by profiling user behaviour.
    • Interpreting complex UBA models poses a significant challenge for analysts.
    • Existing methods lack comprehensive multi-level user behaviour analysis.

    Purpose of the Study:

    • To present a visual analytics approach for understanding user behaviour at individual and group levels.
    • To design a user-centred framework for analysing user activities in digital applications.
    • To address the interpretability challenges in UBA systems.

    Main Methods:

    • Developed a visual analytics framework centered on hierarchical user profiles.
    • Utilized topic modelling to extract and summarize user tasks from session data.
    • Employed a user-centred design approach for the framework.
    • Incorporated interactive visualizations for multi-level analysis.

    Main Results:

    • Analysts effectively conducted exploratory and investigative analysis using the framework.
    • Interactive visual hierarchical user profiles enhanced understanding of user behaviour characteristics.
    • The framework facilitated informed decision-making when evaluating suspicious users and activities.
    • Experts confirmed the utility of the approach in use case evaluations.

    Conclusions:

    • The visual analytics framework significantly improves the interpretability of UBA models.
    • Hierarchical user profiles provide a comprehensive view of user behaviour for analysts.
    • The approach supports effective identification and analysis of anomalous user activities.