Introduction
Financial services play a crucial role in everyday life, requiring expert-level support to meet highly personalized user needs across domains like banking, insurance, and taxation. The emergence of foundation models, especially large language models (LLMs), has introduced new capabilities in communication, reasoning, and personalization that align well with financial decision-making processes. Recently, agentic AI has extended these models by enabling them to autonomously plan, reason, and act across multi-step tasks, making them highly suitable for complex use cases such as financial advising and compliance. Co-located with AAAI’26, this workshop aims to bring together researchers and practitioners to explore the latest advances in agentic AI for a wide range of financial services, fostering discussions and new ideas on design, deployment, ethics, and real-world impact.
Topics of InterestThis workshop encourages submissions of innovative solutions for a broad range of problems in finance. Topics of interest include but are not limited to the following:
The workshop will be a half-day event, featuring keynote speeches, paper presentations, and poster sessions.
AttendanceThe workshop welcomes AAAI conference attendees with an interest in LLM, financial technology, information retrieval, and recommender systems. There are no restrictions on attendance, and the maximum number of attendees will be determined by the room capacity.
Submission GuidelinesSubmission Requirements
Long Papers: 5-7 pages of main content, plus unlimited pages for references followed by appendices (if any).
Short Papers, Extended Abstract, and Enlightening Talks: No more than 4 pages of main content, plus unlimited pages for references followed by appendices (if any).
All submissions should adhere to the AAAI’26 formatting guidelines and will undergo a peer review process.
Submission Site Information
https://openreview.net/group?id=AAAI.org/2026/Workshop/AI-4-Finance
Organizing CommitteeFor more details, visit https://ai-4-finance.pages.dev
Best,
Fengbin Zhu