The rapid dissemination of digital information has exposed financial markets to the risks of disinformation. Although numerous methods exist to detect fake news, they predominantly focus on textual features, often neglecting the significant role of image-based content. This paper introduces a novel framework for detecting financial fake news in Brazilian Portuguese by bridging this gap. The proposed system integrates Natural Language Processing (NLP) with an image-to-text classification strategy: using a Tesseract-based OCR, the system extracts text from images and processes it using the unified pipeline used for text classification. Experiments on Fake.BR, FakeRecogna corpus and BBC News Brasil show that our approach achieves 98% accuracy using BERTimbau Fine Tuned on financial news. These findings underscore the critical importance of analyzing visual text and demonstrate the multimodal strategy is effective for disinformation detection.

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.