A global survey of more than 5,000 consumers reveals that nearly three in four shoppers now use artificial intelligence in their shopping journey, marking a defining moment for ecommerce. However, this adoption creates significant new risks for merchants as AI agents blur traditional accountability lines for fraud and policy disputes.
The research shows shoppers embrace AI assistants for product ideas (45%), review summarization (37%), and price comparison (32%). While only 13% report completing purchases after AI assistant referrals, 70% express at least some comfort with AI agents making purchases on their behalf. More than half (58%) plan to use these tools for gift shopping, indicating the first truly AI-powered holiday season approaches.
When AI shopping agents make purchases, fundamental questions about accountability arise. If a shopper never visits a merchant's website during checkout because an AI agent completed the transaction, who bears liability when disputes occur? This uncertainty creates new risk categories for all parties involved.
Disputed charges could stem from hijacked AI accounts, legitimate customers claiming their AI assistants made errors, or fraudulent actors exploiting system vulnerabilities. In each scenario, liability remains murky and data transparency proves insufficient for traditional fraud prevention approaches.
Fraud operations already enabled by AI will likely exploit gaps in identity verification, account security, customer service claims, and payment flows. What appears as smoother shopping experiences for consumers can quickly become surges in disputes and chargebacks—costs merchants must absorb without clear mechanisms for prevention or recovery.
Payment security represents the primary worry for nearly one in three shoppers (32%), followed by privacy concerns (26%), potential mistakes (18%), and loss of control (17%). Despite these apprehensions, trust in AI approaches parity with traditional channels: 36% of consumers now trust AI to influence purchases, nearly matching the 38% who rely on in-store associates. Only 25% prefer shopping online without AI assistance.
This trust evolution creates tension between consumer convenience and merchant protection. As adoption accelerates—particularly with the rollout of open standards enabling purchases through AI interfaces—merchants face operational challenges without corresponding protective mechanisms.
Given evolving consumer behavior and emerging agentic commerce protocols, merchant fraud teams should focus on several strategic priorities:
Executive education. Fraud teams possess unique positioning to understand risk-reward tradeoffs in agentic commerce. They must proactively educate organizations about new fraud vectors and abuse patterns, ensuring leadership understands full implications before integrating these technologies. Without executive awareness, organizations may adopt agentic commerce without adequate safeguards.
Data transparency advocacy. Sustainable agentic commerce partnerships require technology platforms sharing crucial customer behavioral insights—IP addresses, device information, behavioral signals—with merchants. Without this visibility, merchants cannot make informed risk decisions. Fraud and abuse could overwhelm systems if data asymmetries persist between platforms facilitating transactions and merchants bearing liability.
Networked intelligence leverage. In agentic environments where merchants have less direct customer data, partnering with fraud intelligence platforms becomes critical. These networks can restore context lost in agentic transactions by connecting data points across merchant communities, identifying patterns individual organizations cannot detect independently.
Fraud prevention teams represent natural leaders for agentic commerce safety because they're positioned to see both opportunity and risk. Their expertise focuses not on rejecting innovation but building guardrails enabling confident adoption. They prove essential for ensuring exciting new channels become sustainable revenue drivers rather than fraud vectors.
This leadership role requires shifting from reactive fraud detection to proactive risk architecture. Traditional approaches monitoring individual transactions prove insufficient when AI agents execute purchases without direct merchant interaction. Instead, fraud teams must design systems evaluating AI agent behavior patterns, authentication mechanisms, and dispute resolution frameworks adapted to agentic commerce characteristics.
While current agentic commerce discussion focuses primarily on consumer retail, implications extend to business-to-business procurement and supply chain operations:
Procurement automation risks. As organizations deploy AI agents for supplier discovery and purchasing, similar liability questions emerge. When AI systems negotiate contracts or place orders, who bears responsibility for errors, disputes, or fraudulent transactions?
Authentication challenges. B2B transactions typically involve larger values and more complex approval workflows than consumer purchases. AI agents operating on behalf of procurement teams must integrate with existing authorization structures while maintaining security.
Data sharing requirements. Supply chain platforms enabling agentic commerce must balance efficiency gains against fraud prevention needs. Organizations requiring visibility into AI agent behavior for risk management may resist platforms prioritizing speed over transparency.
Dispute resolution complexity. Business procurement disputes often involve contractual obligations, service level agreements, and relationship management considerations beyond simple payment reversals. AI agent transactions complicate these already nuanced resolution processes.
Organizations preparing for agentic commerce expansion should develop comprehensive frameworks addressing:
Clear liability definitions. Contracts with AI platform providers must explicitly define responsibility for fraudulent transactions, unauthorized purchases, and dispute resolution costs. Ambiguous agreements create unacceptable risk exposure.
Authentication standards. Multi-factor authentication, behavioral analysis, and transaction limits appropriate for AI agent operations differ from traditional user authentication. Organizations need standards adapted to agentic commerce patterns.
Monitoring capabilities. Real-time visibility into AI agent purchasing behavior enables detecting anomalies before significant fraud occurs. Systems must flag unusual patterns—purchase frequency changes, vendor relationship shifts, authorization bypass attempts.
Chargeback protection mechanisms. Given liability uncertainty, organizations need strategies for managing financial exposure from agentic commerce disputes. This may involve insurance products, platform guarantees, or reserve funds absorbing potential losses.
The transformation toward AI-enabled shopping represents significant operational evolution requiring proactive risk management rather than reactive fraud response. Organizations building appropriate guardrails can capture efficiency benefits while managing downside exposure.
Evaluating fraud prevention strategies for AI-enabled commerce? Contact Trax Technologies to explore how data visibility, pattern recognition, and intelligent monitoring enable secure adoption of automated purchasing while protecting against emerging fraud vectors.