Multi-Chain DAO Treasury Management: a Risk and Compliance Optimization Framework for the U.S. Ecosystem
DOI:
https://doi.org/10.70393/6a696574.333834ARK:
https://n2t.net/ark:/40704/JIET.v1n1a02Disciplines:
Computer ScienceSubjects:
Risk and Compliance Optimization FrameworkReferences:
29Keywords:
Multi-chain DAO, Treasury Management, U.S. Web3 Regulation, OFAC Screening, DAO Regulatory Adaptation, U.S. Institutional Investment AccessAbstract
Multi-chain deployment has become a mainstream strategy for U.S.-based DAOs, yet treasury management faces three core bottlenecks: cross-chain liquidity fragmentation, inadequate compliance with U.S. regulations (including OFAC sanctions screening and SEC transparency requirements), and inefficient revenue distribution. Leveraging the incubation practices of over 12 U.S. DAOs (via daos.world) and expertise in multi-chain smart contract development, this study proposes a three-dimensional risk and compliance optimization framework (cross-chain risk hedging + real-time regulatory screening + hierarchical revenue distribution). Empirical testing on 8 U.S. DAOs (operating on Base/Ethereum/Solana, covering AI-focused, meme coin-focused, and investment-focused types) over a 6-month period (September 2025 - February 2026) demonstrates that the framework reduces cross-chain compliance risks by 82.3% (OFAC violation rate drops from 18.0% to 3.2%), increases the annualized treasury return rate by 17.6% (from 4.2% to 5.04%), lowers cross-chain transaction costs by 28.5% (average Gas fee decreases from $12.8 to $9.1), and shortens liquidity adjustment response time from 48 hours to 6 hours. Integrating U.S. regulatory requirements with cross-chain technical logic, this research addresses the theoretical gap in multi-chain DAO treasury management, provides a replicable paradigm for U.S. DAOs to balance compliance, security, and profitability, aligns with the standardization strategy of the U.S. Web3 ecosystem, and is expected to unlock $15-20 billion in potential investment value.
References
[1] Decker, N. (2025). CLEARfund™: The First Smart Contract–Governed, IRS-Compliant, SEC-Regulated SaaS Infrastructure for Multi-Chain Fund Operations.
[2] Bayan, T., Yazici, A., & Banach, R. (2025). Permissionless Blockchain Recent Trends, Privacy Concerns, Potential Solutions and Secure Development Lifecycle. Future Internet, 17(12), 547.
[3] Lin, A. (2025). Low-Barrier Pathways for Traditional Financial Institutions to Access Web3: Compliant Wallet Custody and Asset Valuation Models. Frontiers in Management Science, 4(6), 80-86.
[4] Qi, Z. (2025). Design of a Medical IT Automated Auditing System Based on Multiple Compliance Standards. Innovation in Science and Technology, 4(9), 17-23.
[5] Yin, M. (2025). A Data-Driven Approach for Real-Time Bottleneck Detection and Optimization in Semiconductor Manufacturing Using Active Period Method and Visualization. Academic Journal of Natural Science, 2(4), 19-26.
[6] Sun, Y., & Ortiz, J. (2024). An ai-based system utilizing iot-enabled ambient sensors and llms for complex activity tracking. arXiv preprint arXiv:2407.02606.
[7] Li, K., Chen, X., Song, T., Zhou, C., Liu, Z., Zhang, Z., Guo, J., & Shan, Q. (2025a, March 24). Solving situation puzzles with large language model and external reformulation.
[8] Sidhu, M. K., Tomar, A., Sharma, R., & Batra, N. (2026). Emerging Paradigms and Advanced Architectures in the Blockchain Metaverse. In Blockchain Enabled Metaverse for Smart Wireless Sensor Networks (pp. 51-80). Cham: Springer Nature Switzerland.
[9] Li, K., Chen, X., Song, T., Zhang, H., Zhang, W., & Shan, Q. (2024). GPTDrawer: Enhancing Visual Synthesis through ChatGPT. arXiv preprint arXiv:2412.10429.
[10] Chen, T. (2025). Digital Transformation in Art Education: The Practice and Innovation of Online Courses. Research and Advances in Education, 4(6), 10-14.
[11] Chen, Y. (2025). Interpretable Automated Machine Learning for Asset Pricing in US Capital Markets. Journal of Economic Theory and Business Management, 2(5), 15-21.
[12] Rahman, A., Shi, V., Ding, M., & Choi, E. (2022). Systematization of knowledge: Synthetic assets, derivatives, and on-chain portfolio management. arXiv preprint arXiv:2209.09958.
[13] Chen, Y., & Xu, J. (2026). Deep Learning for US Bond Yield Forecasting: An Enhanced LSTM–LagLasso Framework. ICCK Transactions on Emerging Topics in Artificial Intelligence, 3(2), 61-75.
[14] Pang, F. (2020, November). Research on Incentive Mechanism of Teamwork Based on Unfairness Aversion Preference Model. In 2020 2nd International Conference on Economic Management and Model Engineering (ICEMME) (pp. 944-948). IEEE.
[15] Liu, Z. (2025). A Reproducible Baseline for Forecasting High-Frequency Realized Volatility with Order-Flow Features. Journal of Economic Theory and Business Management, 2(6), 8-18.
[16] Chen, Y. (2025). Artificial Intelligence in Economic Applications: Stock Trading, Market Analysis, and Risk Management. Journal of Economic Theory and Business Management, 2(5), 7-14.
[17] Chen, Y. (2025). Daily Asset Pricing Based on Deep Learning: Integrating No-Arbitrage Constraints and Market Dynamics. Journal of Computer Technology and Applied Mathematics, 2(6), 1-10.
[18] Yin, M. (2025). Predictive Maintenance of Semiconductor Equipment Using Stacking Classifiers and Explainable AI: A Synthetic Data Approach for Fault Detection and Severity Classification. Journal of Industrial Engineering and Applied Science, 3(6), 36-46.
[19] Qi, Z. (2025). Root Cause Tracing Algorithm and One-Click Repair Mechanism for Medical Server Failures. Journal of Progress in Engineering and Physical Science, 4(5), 43-48.
[20] Sarang, P., & Nadkar, L. (2025). Security Challenges. In Blockchain Without Barriers: An Authentic Guide to Blockchain Interoperability (pp. 35-63). Cham: Springer Nature Switzerland.
[21] Ni, D. (2025). Research on the Construction and Efficacy Optimization of Intelligent Logistics Automation Technology System in Cross-Border Scenarios. Frontiers in Management Science, 4(5), 35-41.
[22] Yin, M., & Frank, L. F. (2026). Multi-Modal Fusion for Yield Optimization: Integrating Wafer Maps, Metrology, and Process Logs with Graph Models. ICCK Transactions on Emerging Topics in Artificial Intelligence, 3(1), 45-60.
[23] Meneguzzo, S., Schifanella, C., Gatteschi, V., & Destefanis, G. (2025). Evaluating DAO Sustainability and Longevity Through On-Chain Governance Metrics. arXiv preprint arXiv:2504.11341.
[24] Quanzhen, D. (2025). Preparation of Biodegradable Eco-Friendly Pressure-Sensitive Adhesive Materials and Their Compatibility with Water-Based Printing Processes. Academic Journal of Engineering and Technology Science, 8(4), 150-155.
[25] Iyer, R., Maralapalle, V., Patil, D., & Irfan, M. (2024). The future of smart contracts: Pioneering a new era of automated transactions and trust in the digital economy. In AI-driven decentralized finance and the future of finance (pp. 225-251). IGI Global.
[26] Qi, Z. (2025). Design and Practice of Elastic Scaling Mechanism for Medical Cloud-Edge Collaborative Architecture. Journal of Innovations in Medical Research, 4(5), 13-18.
[27] Tan, Z., Li, Z., Liu, T., Wang, H., Yun, H., Zeng, M., ... & Jiang, M. (2025). Aligning large language models with implicit preferences from user-generated content. arXiv preprint arXiv:2506.04463.
[28] Notland, J. S., Li, J., & Nowostawski, M. (2025). An Empirical Study on Deployment in Cross-Chain Decentralised Autonomous Organisations. Blockchain: Research and Applications, 100291.
[29] Decker, N. (2025). The Harvest Labs Doctrine™: Replacing Speculative Crypto with Constitutional Yield Architecture for National Solvency and Infrastructure Restoration. Available at SSRN 5288465.
Downloads
Published
How to Cite
Issue
Section
ARK
License
Copyright (c) 2026 The author retains copyright and grants the journal the right of first publication.
This work is licensed under a Creative Commons Attribution 4.0 International License.
