The paper discusses the critical need for explainability in Large Language Models (LLMs) applied in finance. It introduces various interpretability methods that not only demystify the workings of these models (through mechanisms such as Sparse Autoencoders) but also highlight their application in finance-specific scenarios. Key areas of application include enhancing sentiment analysis, identifying biases in model predictions, and improving trade decision-making processes. The ability to interpret LLMs is essential in gaining trust and ensuring compliance in financial applications, where decisions can have significant real-world impacts. The integration of interpretability methods could lead to better transparency and reliability in finance-related AI tools.