Energy Efficient ML UX Design: Balancing Sustainability and User Engagement in Fintech

  • Authors

    • Amanda Davis Business Development Manager, Nexus Solutions, USA. Author
    • Kevin Taylor Senior Project Manager, Prime Innovations, USA. Author

    DOI:

    https://doi.org/10.67228/3071561X/IJIRHT-2019PII3C7L

    Published 11-04-2019

  • Energy-Efficient Machine Learning, User Experience (UX) Design, Fintech, Sustainability, Green AI, Model Optimization, User Engagement, Sustainable Technology, Computational Efficiency, Personalized Finance Apps

    Issue

    Vol. 2 No. 2 (2019)

    Section

    Articles

    How to Cite

    [1]
    A. Davis and K. Taylor, “Energy Efficient ML UX Design: Balancing Sustainability and User Engagement in Fintech”, IJIRHT, vol. 2, no. 2, pp. 01–10, Nov. 2019, doi: 10.67228/3071561X/IJIRHT-2019PII3C7L.

  • Abstract

    The rapid adoption of machine learning (ML) in fintech has revolutionized user experiences, enabling personalized services, fraud detection, and financial insights. However, the increasing computational demands of ML models contribute significantly to energy consumption, raising sustainability concerns. This paper explores the intersection of energy-efficient ML and user experience (UX) design within fintech applications. We analyze techniques to optimize ML models for reduced energy footprints while maintaining high user engagement and trust. By proposing a balanced framework that integrates sustainability principles with UX best practices, the study aims to guide fintech developers in creating applications that are both environmentally responsible and user-friendly. Case studies illustrate practical implementations, and future directions highlight the potential of emerging technologies and regulatory efforts. Ultimately, this paper advocates for a holistic approach to fintech innovation that prioritizes both ecological impact and user satisfaction.

  • References

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