AI-Augmented Risk Analytics: Immersive UX in Cloud‑Native Compliance Tools

  • Authors

    • Dr. Linda Martinez Associate Professor, University of California, Berkeley, USA. Author
    • Dr. Mark Richardson Professor, Harvard University, USA. Author

    DOI:

    https://doi.org/10.67228/3071561X/IJIRHT-2019PII2H5P

    Published 08-04-2019

  • AI-Augmented Analytics, Risk Management, Compliance Automation, Immersive UX, Cloud-Native Architecture, Regulatory Technology (Regtech), Human-AI Collaboration, Intelligent Compliance Tools, Real-Time Risk Detection, User-Centered Design

    Issue

    Vol. 2 No. 2 (2019)

    Section

    Articles

    How to Cite

    [1]
    L. Martinez and M. Richardson, “AI-Augmented Risk Analytics: Immersive UX in Cloud‑Native Compliance Tools”, IJIRHT, vol. 2, no. 2, pp. 01–10, Aug. 2019, doi: 10.67228/3071561X/IJIRHT-2019PII2H5P.

  • Abstract

    The increasing complexity of regulatory environments and data ecosystems necessitates innovative approaches to compliance and risk management. This paper explores how AI-augmented risk analytics, when integrated into cloud-native platforms, can significantly enhance the accuracy, efficiency, and adaptability of compliance tools. By incorporating immersive user experience (UX) design—through interactive dashboards, real-time data visualizations, and intuitive workflows—organizations can better engage with compliance processes and risk insights. The paper examines the convergence of artificial intelligence, immersive UX, and cloud technologies, illustrating their combined impact on regulatory efficiency and decision-making. Case studies and architectural patterns are presented to highlight best practices and future directions for scalable, user-centric compliance solutions.

  • References

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    [9] Forrester. (2023). Cloud-native platforms in modern compliance and audit frameworks.

    [10] European Union AI Act (2024 Draft). Legal and ethical guidance on AI transparency.

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    [14] PwC. (2022). Measuring KPIs in AI-driven compliance systems.

    [15] MIT Sloan Management Review. (2023). UX fatigue and cognitive overload in AI platforms.

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