This paper presents our contribution to the Financial Document Causality Detection (FinCausal) task 2025. The FinCausal challenge centers on the extraction of cause-and-effect relationships from financial texts written in both English and Spanish. We introduce KULFi, a novel Knowledge Utilization framework designed to augment the capabilities of Large Language Models (LLMs) by leveraging the expertise of more advanced reasoning models. Through the utilization of Teacher LLMs to generate task-specific instructions, KULFi optimizes the performance of Student LLMs via automated prompt optimization. We evaluate the efficacy of KULFi on the Financial Document Causality Detection Task, where Student LLM achieves a similarity score comparable to human-guided prompt optimization for the same LLM, demonstrating significant improvements in causal reasoning performance. Our results demonstrate that KULFi enables effective knowledge transfer from more robust models to less capable ones, as well as efficient learning from training data, minimizing the need for human input in prompt design and enabling more precise causal analysis in financial contexts. Our system attained SAS and Exact Match scores of 0.92 and 0.35 on the English dataset, and 0.92 and 0.09 on the Spanish dataset, respectively. This framework has far-reaching implications, with potential applications in enhancing decision-making across complex financial environments.