Trustworthy Artificial Intelligence in Financial Decision-Making: A Systematic Review of Explainability, Fairness, and Accountability

Minhao Li and Shuyang Xu

Journal of Sustainability, Policy, and Practice, 2026, vol. 2, issue 3, 104-114

Abstract: The rapid adoption of artificial intelligence (AI) in financial services has introduced critical concerns regarding the transparency, equity, and governance of algorithmic decision-making. This paper presents a systematic review of 43 peer-reviewed studies published between 2018 and 2024, examining three core dimensions of trustworthy AI in finance: explainability, fairness, and accountability. The review synthesizes findings across credit scoring, fraud detection, risk management, and algorithmic trading domains. Results indicate that post-hoc explainability methods such as SHAP and LIME dominate current implementations, while fairness-aware approaches remain underexplored relative to performance optimization. A persistent trade-off between predictive accuracy and fairness is documented across multiple application contexts. This paper contributes a structured analytical framework and identifies gaps that warrant future investigation under evolving regulatory mandates including the EU AI Act.

Keywords: Trustworthy AI; Financial Decision-Making; Explainable AI; Algorithmic Fairness (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:dba:jsppaa:v:2:y:2026:i:3:p:104-114

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