This article examines the integration of artificial intelligence in financial evaluation and the vital role of explainability in building trustworthy decision support systems. As AI transforms traditional financial evaluation from forecasting to portfolio management, the inherent opacity of sophisticated algorithms creates tension with the financial sector’s transparency requirements. The discussion explores how Explainable AI techniques—particularly SHAP values and LIME—enable financial professionals to understand AI-generated insights while maintaining regulatory compliance. Through examining real-world implementations, the article demonstrates quantifiable benefits of explainable models in reducing false positives, improving analyst confidence, and accelerating regulatory approval. The evaluation extends to comprehensive Responsible AI frameworks encompassing fairness and bias mitigation, privacy-preserving techniques, and adversarial resilience mechanisms. The discussion addresses how generative AI assistants revolutionize document evaluation by automating summarization and data extraction while confronting critical security challenges, including prompt injection attacks, data leakage, and regulatory compliance complexities. The article emphasizes human-in-the-loop paradigms and tiered governance frameworks that successfully balance innovation with appropriate oversight, while examining real-time explainability challenges and monitoring requirements. Forward-looking perspectives on regulatory harmonization and the convergence of explainable, privacy-preserving, and robust AI systems demonstrate the evolution toward trustworthy financial AI implementations.
Keywords: Artificial Intelligence, Financial Analysis, Human-AI collaboration, SHAP values, explainable AI
