This paper describes our approach for the FinCausal 2025 English Shared Task, aimed at detecting and extracting causal relationships from the financial text. The task involved answering context-driven questions to identify causes or effects within specified text segments. Our method utilized a consciousAI RoBERTa-base encoder model, fine-tuned on the SQuADx dataset. We further fine-tuned it using the FinCausal 2025 development set. To enhance the quality and contextual relevance of the answers, we passed outputs from the extractive model through Gemma2-9B, a generative large language model, for answer refinement. This hybrid approach effectively addressed the task’s requirements, showcasing the strength of combining extractive and generative models. We (Team name: Sarang) achieved outstanding results, securing 3rd rank with a Semantic Answer Similarity (SAS) score of 96.74% and an Exact Match (EM) score of 70.14%.