Automatic summarization of financial news in Portuguese lacks reliable reference-free evaluation metrics. While LLM-as-a-Judge approaches are gaining traction, their correlation with human perception in specialized domains remains under-explored. This work evaluates the efficacy of Question Answering (QA) based metrics against a direct LLM-as-a-Judge baseline for Portuguese financial news. We propose a pipeline comparing Lexical, Binary, and Semantic (LLM-based) QA scoring methods, validated against a human ground truth of 50 news items annotated for Faithfulness and Completeness. Our results show that granular QA metrics significantly outperform the monolithic LLM-Judge in evaluating Completeness, with QA-Binary achieving the highest rank correlation (ρ ≈ 0.49 with pessimistic human aggregation). For Faithfulness, we observe a strong ceiling effect in human evaluation, yet the Semantic QA metric demonstrated a "super-human" ability to detect subtle hallucinations (e.g., temporal shifts) missed by annotators. We conclude that decomposing evaluation into atomic QA pairs is superior to holistic judging for the Portuguese financial domain.