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The emergence of agentic commerce and payments, leveraging advanced artificial intelligence, is poised to revolutionize how consumers shop and transact. From AI-powered shopping assistants to fully autonomous agents executing purchases based on human instructions, this technology promises unprecedented convenience. However, a critical question arises: how smooth will this transition be for merchants, consumers, and financial institutions, and who shoulders the blame when these digital agents inevitably make mistakes?
The Unforeseen Risks of Autonomous Commerce
A recent white paper from the Consumer Bankers Association (CBA) and Davis Wright Tremaine LLP highlights significant risks for all parties involved, especially as agentic payments evolve without robust safeguards inherent in traditional payment systems. The core issue revolves around liability. Existing regulations, such as the Electronic Fund Transfer Act, typically limit consumer liability for unauthorized transactions. Yet, this protection may not extend to errors made by AI agents acting on a consumer’s behalf. Consumers could find themselves financially responsible for bot-generated missteps.
For banks, the implications are substantial. The white paper stresses that banks are likely to become the primary point of contact for customers seeking recourse when agentic transactions go awry. Despite not being the direct cause of the bot’s error, customers will expect their banks to resolve disputes and make them whole for improper payments, a responsibility that could drastically increase operational burdens.
The Rise of Agentic AI: What You Need to Know
Consumer interest in agentic commerce is rapidly expanding. Adobe reported a nearly 700% increase in shopping via GenAI-powered chat services and browsers during the 2025 holiday season compared to 2024. A PYMNTS.com report further indicates that consumers are ready to delegate significant purchasing and financial tasks to AI agents, driven not by novelty, but by the desire for reduced friction. However, broad adoption hinges on establishing “payments-grade trust.”
This trust is critical, especially as technologies like OpenClaw, an “AI that does things,” emerge, threatening to bypass existing controls and potentially creating new vulnerabilities. Major payment players, including Mastercard, Visa, PayPal, and Google, are actively pushing the boundaries of agentic commerce and payments, signaling its inevitable integration into daily financial life.
Defining Autonomous Transactions and Regulatory Gaps
The CBA white paper, “Agentic AI Payments: Navigating Consumer Protection, Innovation, and Regulatory Frameworks,” defines agentic tools as those that orchestrate and execute transactions autonomously, without direct human intervention. Imagine instructing an AI: “Find and buy the best laptop under $500 for a 13-year-old.” The AI then independently researches, selects, and completes the purchase.
The widespread embrace of such digital commerce depends heavily on consumers’ belief that the long-standing protections for credit and debit cards, rooted in federal law and payment network policies, will still apply. The crucial challenge arises with new payment rails, such as cryptocurrencies, which often lack the established mechanisms for refunds, chargebacks, and dispute resolution that consumers currently rely on. Alarmingly, the paper suggests that agentic payments could even accelerate the adoption of these less regulated payment methods, including stablecoins, pushing consumers, merchants, and banks into largely uncharted and unregulated territory. Furthermore, immediate governmental action to regulate AI appears unlikely, with current administrations favoring a hands-off approach to allow market innovation.
Examining Consumer Vulnerabilities
Agentic commerce introduces several distinct risks for consumers:
- Agent Bias: AI agents might not always act in the consumer’s best interest. They could favor specific merchants or payment rails due to underlying incentives for developers, potentially leading to suboptimal deals for the user.
- Transactional Errors: Combinations of poor instructions or insufficient training data can result in incorrect purchase decisions. Current AI often lacks the contextual awareness to prevent obvious mistakes, like buying bulk frozen food for a user with limited freezer space. The paper also hypothesizes a “Tickle Me Elmo” trap, where numerous agents simultaneously target the same product, causing price spikes, availability issues, or overwhelming merchant systems.
- Data Breaches: To function effectively, powerful AI agents require extensive access to sensitive consumer data, including financial history, account balances, health information, and past purchases. Many agentic payment applications may be developed by non-financial entities not subject to established data protection laws like the Gramm-Leach-Bliley Act, creating significant privacy gaps.
- Malicious Agents: The threat of criminals developing fraudulent AI agents that mimic legitimate tools to steal financial information or execute unauthorized transactions is a serious concern.
- Exceeding Authority: While consumer liability for unauthorized electronic fund transfers is usually capped, an exception exists if a consumer gives an “access device” to another person who then exceeds their authority. The legal ambiguity of whether an AI agent constitutes a “person” in this context creates a significant grey area regarding liability.
Key Risks for Financial Institutions
The ripple effects of consumer and merchant issues will heavily impact banks:
- Increased Dispute Resolution: If agents frequently exceed instructions or generate disputed transactions, banks face a surge in dispute volumes and potentially higher reimbursement costs. Autonomous purchasing decisions without human oversight could also lead to more merchant fraud or unfulfillable orders.
- Bypassing Customer Service: There’s a risk that AI agents will default to banks’ dispute and chargeback systems rather than engaging with merchant customer service, further burdening financial institutions.
- Compliance Exposure: AI agents trained on biased data could inadvertently perpetuate discrimination or redlining when assessing creditworthiness, leading to violations of fair-lending laws.
- Enhanced Fraud Risk: When third-party developers create agents operating through merchant APIs or open banking infrastructure, banks may have limited visibility into agent behavior, increasing their exposure to fraud.
The Quest for Solutions: A Work in Progress
Addressing these complex risks presents a significant challenge. The white paper acknowledges that many potential solutions face hurdles in the current political and regulatory landscape. Proposals like making the Federal Trade Commission the primary regulator for AI agents are unlikely to gain traction quickly. Furthermore, the speed of agentic purchasing means many conventional regulatory approaches may be too slow to be effective.
Nevertheless, some nascent solutions are being considered:
- An Agentic AI Version of RESPA: Drawing inspiration from the Real Estate Settlement Procedures Act, which prohibits kickbacks and unearned fees in real estate transactions, a similar framework could be developed for broader consumer commerce involving AI agents.
- State-Level Licensing: Licensing AI providers, akin to how money transmitters or lenders are regulated at the state level, is another idea, though some fear it could overly burden nascent AI startups.
- Industry Self-Regulation: Establishing voluntary industry standards for AI agent operations could provide a quicker, albeit potentially less enforceable, path to risk mitigation.
As AI continues to blur the lines between reality and automation, the financial industry must proactively address these emerging challenges to maintain trust and ensure consumer protection in the age of autonomous commerce.
Source: thefinancialbrand.com