In the rapidly evolving financial technology landscape, Retrieval-Augmented Generation (RAG) has emerged as a powerful tool for enhancing Large Language Models (LLMs) with domain-specific knowledge. However, as financial decision-making becomes increasingly complex, RAG fails to capture the intricate causal relationships crucial for accurate predictions and robust strategy formulation. This talk introduces the concept of using causal AI with RAG, it integrates causal inference techniques such as do-calculus, counterfactual reasoning, and causal discovery algorithms into the RAG pipeline. By incorporating these methods, this methodology improves the accuracy of financial models. It enhances their interpretability, allowing for better identification of key market drivers and more reliable predictions of economic policy impacts.
Transparency and explainability are the two major areas of critical concern in the finance sector. By explicitly modeling cause-and-effect relationships, this combined framework could provide clearer insights into the reasoning behind financial predictions and recommendations. This approach enables financial analysts to better understand systemic risks, optimize portfolios with greater confidence, and make more informed decisions in areas such as risk assessment, algorithmic trading, and economic forecasting. Moreover, the enhanced explainability of these models aligns with increasing regulatory demands for transparency in AI-driven financial systems, potentially reducing legal and reputational risks associated with ""black box"" AI models. Through real-world applications and quantitative improvements, this talk will demonstrate how Causal AI + RAG represents the next frontier in AI-powered financial analysis, offering a powerful toolkit for more accurate, interpretable, and actionable insights in an increasingly complex financial landscape.