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.
Call for Papers
Contact us at: agenticai4fs@gmail.com
This 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:
- Research, implementation, or deployment of agentic AI in financial decision-making
- Multi-agent systems for market/user simulation and trading
- Foundation models for banking, trading, and other financial services
- Explainability and trust in financial AI agents
- Agentic AI for regulatory compliance and auditing
- Conversational financial agents and advisors
- Autonomous document processing and financial knowledge extraction
- Risk management with agentic AI, e.g., fraud detection and credit assessment
- Agentic workflows/architectures in financing platforms
- Ethical and policy aspects of agentic AI in finance
Accepted submission formats include the following:
- Long Papers: 5-7 pages of main content, plus unlimited pages for references followed by appendices (if any).
- Short Papers, Extended Abstract, and Lightening 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. Detailed submission instructions will be provided soon.
Accepted papers will be presented as posters during the workshop and archived on OpenReview for public access (without proceedings). A small number of accepted papers will be selected to be presented as contributed talks. We also welcome submissions of unpublished papers, including those that are submitted/accepted to other venues if that other venue allows so.
Submission site: https://openreview.net/group?id=AAAI.org/2026/Workshop/AI-4-Finance
Any questions may be directed to the workshop e-mail address: agenticai4fs@gmail.com
Key Dates
Workshop Paper Submission Due Date:
22 Oct 2025 (AoE)
Notification of Paper Acceptance: 28 Nov 2025 (AoE)
AAAI'26 Workshops: 26 or 27 Jan 2026 (TBD)
At least one author of each accepted paper *must* travel to the AAAI venue in person, and multiple submissions of the same paper to other AAAI workshops are forbidden.
Detailed Workshop Schedule
The detailed schedule will be released after submissions are finalized.
Keynote Speakers
TBD
TBD
Generalist Anomaly Detection in Financial Networks
Graph anomaly detection (GAD) is one of the main techniques for identifying abnormal activities in financial networks. While numerous GAD methods have been introduced over the years, they are based on a one-model-for-one-dataset paradigm, training one detection model on each target dataset individually. These methods require availability of large training data and skillful model training on each dataset, and even worse, the resulting models often cannot generalize beyond the training distribution. Thus, they become infeasible in high-stake domains such as finance where large-scale training on the target dataset may not be possible due to issues such as data privacy and costly data annotation. This calls for generalist models for GAD, i.e., to train one single GAD model that can generalize to detect anomalies in diverse datasets. Current graph foundation models (GFMs) have achieved remarkable success in different graph tasks but struggle to generalize to the GAD task. This limitation arises from their difficulty in learning generalized knowledge for capturing the inherently infrequent, irregular, and heterogeneous abnormality patterns in graphs from different applications. This talk will introduce some of our recent efforts that build generalist GAD models for generalized detection across datasets and domains under zero-shot or few-shot settings.
Build Autonomous and Collaborative Financial Agents using NVIDIA's Agentic AI Stack
Financial services are entering a new era where autonomous agents make intelligent decisions, adapt to market conditions, and operate within strict governance frameworks. This session explores how NVIDIA's open-source agentic AI ecosystem enables developers to build production-ready autonomous systems for financial services. We'll journey through the complete lifecycle of agentic AI in finance, from model selection through Nemotron family to specialized agent development. The session covers four critical dimensions: autonomous decision-making, compliance and governance through explainable systems, multi-agent orchestration, and intelligent data integration and knowledge graphs.
Accepted Oral Papers
TBD
Accepted Posters
TBD
Organization Committee
Rocky (Tong) Chen
The University of Queensland
Hongxu Chen
Commonwealth Bank of Australia
Joel Mackenzie
The University of Queensland
Fengbin Zhu
National University of Singapore
Luiz Pizzato
Commonwealth Bank of Australia
Ritchie Ng
Commonwealth Bank of Australia
Anna Leontjeva
Commonwealth Bank of Australia