Human-Centered Explainable Ai for Improving Trust in Automated Financial Decision-Making Systems

  • Authors

    • Muthukumaran Research Scholar, India. Author

    DOI:

    https://doi.org/10.67228/30715628/IJMIET-V9I3P103

    Published 07-12-2026

  • Explainable Artificial Intelligence, Human-Cantered AI, Financial Decision-Making, Algorithmic Transparency, Trust In AI, Credit Scoring, Algorithmic Fairness, Responsible AI, Counterfactual Explanations, Financial Technology

    Issue

    Vol. 9 No. 3 (2026)

    Section

    Articles

  • Abstract

    This article proposes a human-cantered Explainable Artificial Intelligence (XAI) framework to improve trust in AI-driven financial decision-making systems, including credit scoring, loan approvals, fraud detection, and risk assessment. Although AI enhances efficiency and predictive accuracy, its lack of transparency raises concerns about fairness, accountability, and user confidence. The proposed framework integrates interpretable machine learning, explanation quality assessment, fairness evaluation, human oversight, and continuous monitoring. It examines various explanation methods and emphasizes that explanations should be understandable, accurate, fair, and relevant to affected individuals. The study provides a conceptual foundation for responsible AI governance in financial services, with further empirical research required to validate the framework’s effectiveness.

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