The Liability Gap in Agentic Finance
Imagine this scenario: A user with no trading experience casually tells an AI agent to move $2,000 from his brokerage account into a low-risk, international trade opportunity. No forms. No dashboards. No approvals. The agent scans markets, interprets sentiment, executes a cross-border transaction, and settles instantly using stablecoin rails. Minutes later, the funds are gone–the agent hallucinated a market trend that didn’t exist!
Was this a user error because the prompt was vague? A software defect because the model reasoned incorrectly? Or a breach of fiduciary duty because an autonomous system acted on someone’s behalf and failed? There is no clean answer because there is a gap.
The agentic economy is witnessing a liability gap without an explicit accountability layer. Autonomous agents can initiate real economic activity but no clear legal or ethical frameworks still exist to determine accountability when something goes wrong. As we move from chatbots that talk to agents that act, the fintech industry is entering a new phase of risk throttled by lawsuits, regulatory backlash, and institutional hesitation. The next chapter of fintech will be decided by answering a simple question: When an AI agent acts independently with real money, who is ultimately responsible?
The Air Canada Precedent
In Moffatt v. Air Canada (2024), a Canadian tribunal confronted a case where Air Canada’s chatbot provided incorrect information to a customer, leading to financial loss. Air Canada argued that its chatbot was a separate entity and that the company should not be held responsible for the chatbot's mistakes. The tribunal rejected this premise asserting that you cannot outsource responsibility to your own software. A company that leverages an AI system to interact with customers should accept that the system speaks for the company.
In travel, the downside of a bad AI response results in a refund, a voucher, a regulatory fine, and in some cases, reputational damage. In FinTech, the blast radius is orders of magnitude larger. An agentic system connected to stablecoin rails can hallucinate market signals triggering automated trades and cross-border transfers in seconds. The Air Canada precedent sends a clear message to financial institutions experimenting with agentic workflows that they cannot disclaim their way out of AI risk. If the agent has the authority to move funds and initiate transactions, the company that empowered it owns the outcome. Financial institutions need to be legally, ethically, and financially accountable for machines that can act faster than humans. That’s why the next competitive advantage in fintech won’t be speed—it will be governance.
Regulators Entering the Fray
Under the EU AI Act, rolling out in phases between 2024 and 2026, many AI systems used in financial services are explicitly classified as “high-risk.” An AI system affecting someone’s financial wellbeing, creditworthiness, or access to capital triggers a higher standard of care. This includes mandatory conformity assessments before deployment, clear documentation of how decisions are made, and human-override mechanisms especially for what are considered irreversible actions. Europe is asserting a narrative that unaccountable autonomy is not acceptable.
While Europe regulates AI risk, Singapore is designing guardrails for agentic behavior. With the launch of its updated Model AI Governance Framework for Agentic AI, Singapore has addressed the problems of unauthorized actions and autonomous reasoning. The framework asks questions like what authority was the agent given, how does it reason beyond its original instructions, and what safeguards exist if it exceeds that authority. Singapore’s approach assumes bounded autonomy. Agents are allowed to act but only within clearly defined scopes with auditable actions.
The U.S. emphasizes algorithmic financial duty by signaling that existing financial obligations still apply even when decisions are delegated to algorithms. An AI system must prioritize a client’s interest the same way a human advisor would. Conflicts of interest should still be considered and institutions remain responsible for outcomes, not just intent.
The Architecture of Trust: Three Solutions
To bridge the liability gap, the industry needs three foundational standards to establish an accountability layer enabling a financial system where agents cannot outrun the legal structures that make markets possible in the first place.
When an AI agent moves money, there must be a cryptographically verifiable record of why it did so which will be established by Proof of Intent (POI). It generates a signed, privacy-preserving intent artifact that can later answer a critical question: Was this action a reasonable execution of the user’s instruction—or a hallucination? POI serves as a log linking the user’s original prompt or directive, the agent’s interpretation of that instruction, and the action ultimately taken.
The use of Autonomous circuit breakers would leverage smart contracts and policy engines to enforce constraints like maximum value per transaction, cumulative daily or weekly limits and context-based escalation (e.g., first-time vendors, cross-border transfers). Some thresholds could even require a biometric or human confirmation that cannot be faked by the agent itself.
An Agent Identity will serve as a verifiable digital passport that encodes properties such as who created the agent, who authorized it, what permissions it has, and which institution is ultimately accountable for its actions. This will ensure that agents are traceable to a responsible legal entity without which there will be no authority for the agent to move funds.
Trust in the Agentic Economy
We are witnessing the birth of a financial system where conversational interfaces and programmable money converge–allowing people to talk to software that understands intent, reasons autonomously and move funds instantly across the globe. Trust is a critical element of this agentic economy. Without defined authority, identity and accountability, the agentic economy will continue to invite hesitation from institutions and regulators. The solution is better-governed agents that can answer the question–When something goes wrong, who owns the outcome?