The rapid advancements in large language models (LLMs) and generative AI more broadly have significantly impacted the interpretation and utilization of unstructured financial data, enabling a wide range of applications in finance and accounting. While LLMs have unlocked new possibilities in this domain, NLP techniques more broadly remain vital for various applications such as financial analysis, case studies, price forecasting, portfolio optimization, and analyzing financial reports and alternative data. They are also crucial in financial risk modeling, such as credit assessment, bankruptcy detection, and M&A target prediction using news sentiment and topic detection.

However, LLMs and NLP techniques face challenges in the financial domain, including the limited context window of LLMs, the complexities of converting unstructured data into structured formats, hallucinations, and the need for interpretability in financial applications. This workshop aims to bridge the gap between technological advancements in computer science and the specific needs of finance and accounting. By focusing on methodologies such as fine-tuning, retrieval-augmented generation (RAG), prompt engineering, and NLP, we aim to showcase how these techniques can improve the accuracy and relevance of insights derived from financial and accounting data. The ultimate goal is to foster interdisciplinary collaboration, enhance industry solutions, and contribute to academic research in finance and accounting.

The workshop will be held in conjunction with ICAIF ’25.

There is no length limit or strict format requirement. While formatting according to ACM guidelines is recommended (see below), we encourage submissions in an open format to allow the authors to focus on content with formatting restrictions. Submissions should be in PDF format and can be directly submitted as they are.

The workshop is non-archival and will not have official proceedings.