Overview
Thanks to the AI4Finance Foundation open source community for their support.
Introduction
As AI continues to advance at a fast pace, more FinAI agents are being developed for the finance sector, such as FinRL trading agents [1,2,3], FinGPT agents [4,5] with multimodal capabilities [6], and regulatory reporting agents [7]. The Secure FinAI Contest 2026 encourages the development of FinAI agents based on the frameworks FinRL [2,3] and FinGPT [4].
We design four tasks. These challenges allow contestants to participate in various financial tasks and contribute to secure finance using state-of-the-art technologies with privacy-preserving and verifiable computation frameworks. We welcome students, researchers, and engineers who are passionate about finance, machine learning, and security to partake in the contest.
Tasks
Each team can choose to participate in one or more tasks. The prizes will be awarded for each task.
Task I: TBD
Task II: TBD
Task III：TBD
Task IV：TBD
[1] Wang, Keyi, et al. "FinRL Contests: Data‐Driven Financial Reinforcement Learning Agents for Stock and Crypto Trading." Artificial Intelligence for Engineering (2025). [IET] [arXiv]
[2] Liu, Xiao-Yang, et al. "Finrl-meta: Market environments and benchmarks for data-driven financial reinforcement learning." Advances in Neural Information Processing Systems 35 (2022): 1835-1849. [NeurIPS]
[3] Liu, Xiao-Yang, et al. "FinRL: A deep reinforcement learning library for automated stock trading in quantitative finance." arXiv preprint arXiv:2011.09607 (2020). [arXiv] [NeurIPS 2020]
[4] Liu, Xiao-Yang, et al. "Fingpt: Democratizing internet-scale data for financial large language models." arXiv preprint arXiv:2307.10485 (2023). [arXiv]
[5] Tian, Felix, et al. "Customized fingpt search agents using foundation models." Proceedings of the 5th ACM International Conference on AI in Finance. 2024. [ACM]
[6] Yanglet, Xiao-Yang Liu, Yupeng Cao, and Li Deng. "Multimodal financial foundation models (mffms): Progress, prospects, and challenges." arXiv preprint arXiv:2506.01973 (2025). [arXiv]
[7] Han, Shijie, et al. "Xbrl agent: Leveraging large language models for financial report analysis." Proceedings of the 5th ACM International Conference on AI in Finance. 2024. [ACM]
[8] Peng, Xueqing, et al. "MultiFinBen: A Multilingual, Multimodal, and Difficulty-Aware Benchmark for Financial LLM Evaluation." arXiv preprint arXiv:2506.14028 (2025). [arXiv]
Contact
Contact email: finrlcontest@gmail.com
Contestants can communicate any questions on