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

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.