In the banking sector, Trend and Risk Analysis are essential tasks. Analysts are routinely required to examine documents to extract insights, identify trends, and advise investors on suitable actions. Most of the time these documents contain not only text but also images and tables, making them challenging to analyze using traditional Natural Language Processing techniques. One tool that can facilitate the analysis of visually-rich documents is multimodal Large Language Models (LLMs). These models are trained on a vast corpus of documents and other data sources, enabling them both to generate human-like text and retain knowledge embedded within documents. To accelerate the document analysis process, banking organizations are interested in leveraging these models to integrate knowledge into the LLM without sharing the original documents.