Prompt Engineering Frameworks For Financial Risk Intelligence Using Llms

Abstract

Financial institutions increasingly rely on artificial intelligence to improve risk assessment, regulatory compliance, and decision-making processes. Large Language Models (LLMs) have emerged as powerful tools for analyzing complex financial information; however, the quality of their outputs is highly dependent on the design of prompts used to guide their reasoning. This study proposes and evaluates a hypothetical framework for Prompt Engineering in Financial Risk Intelligence, focusing on the effectiveness of different prompting strategies, including Zero-Shot Prompting, Few-Shot Prompting, Chain-of-Thought Prompting, Role-Based Prompting, Retrieval-Augmented Prompting, and Governance-Aware Prompting. A simulated financial dataset containing credit reports, market indicators, transaction records, and regulatory documents was utilized to assess model performance across multiple risk intelligence tasks. The evaluation considered key metrics such as accuracy, precision, recall, F1-score, explainability, consistency, compliance adherence, and expert satisfaction. The findings indicate that advanced prompt engineering techniques significantly enhance the performance of LLMs in financial risk analysis. Governance-Aware Prompting and Retrieval-Augmented Prompting achieved the highest levels of predictive accuracy, transparency, and regulatory alignment, demonstrating their suitability for deployment in regulated financial environments. The study highlights the importance of structured prompt design in improving the reliability, interpretability, and governance of AI-driven financial risk systems.