LLMs and RAG for FinancialData in the FinTech Domain

Abstract. This thesis investigates the implementation and impact of Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) in financial risk management. Through qualitative research methods and iterative prototype development in a financial institution, this thesis explores how these technologies can improve data accessibility and decision-making processes for risk management professionals. The findings reveal both the potential and challenges in the implementation of these technologies in financial environments. Although risk managers showed great enthusiasm and trust in the technology, successful implementation required substantial manual engineering effort in data normalization, semantic modeling, and query generation. Key challenges included handling ambiguous natural language queries and maintaining accuracy in financial calculations. The thesis demonstrates that while LLMs and RAG can improve data accessibility, their effective deployment requires careful attention to domain-specific requirements and human factors.The research contributes to the growing body of knowledge on practical applications of AI in financial services by providing insight into implementation challenges, user acceptance, and system design requirements. These findings have important implications for financial institutions considering similar implementations and suggest directions for future research to improve the reliability and effectiveness of AI-assisted financial risk management tools.