This paper explores the use of large language models (LLMs) and prompt engineering to detect causal relationships in financial disclosures. The task was part of the FinCausal 2025 shared competition, which focuses on identifying cause-and-effect relationships in financial texts across languages. The study demonstrates the effectiveness of LLMs, specifically LLaMA 3.2, in tackling causality detection in English and Spanish financial reports. The paper introduces various prompt engineering techniques, including zero-shot, few-shot, and chain-of-thought (CoT) prompting, to improve performance. For English, the best results were achieved using the Few-Shot + CoT approach, while for Spanish, the Few-Shot method provided strong semantic alignment despite lower exact match accuracy. The evaluation used two metrics: Exact Match (EM) and Semantic Alignment Score (SAS). The results showed high SAS scores for both languages, indicating good semantic understanding, with English performing particularly well. The study emphasizes the importance of tailored prompt engineering techniques to handle language-specific nuances in financial contexts and suggests future research directions, including fine-tuning LLaMA 3.2 and testing additional LLM architectures to enhance multilingual causality detection in financial texts.