Making LLMs Better at Financial Reasoning

Can large language models (LLM) handle complex financial reasoning tasks that require multi-step logic, market knowledge and regulatory adherence? In his December 2024 paper entitled "Large Language Models in Finance: Reasoning", Miquel Noguer I Alonso surveys and extends techniques for enhancing LLM reasoning capabilities. He presents detailed finance-specific coding examples, including dynamic portfolio optimization, scenario stress testing, regulatory compliance analysis and credit risk assessment. He addresses key challenges in scalability, interpretability and bias mitigation. Based on his knowledge and experience with LLMs and other analysis tools, he concludes that: