The impact of fraud has become increasingly punishing for risk managers. Industry projections suggest global chargeback costs could reach $33 billion in 2025, reaching up to $41 billion by 2028. But the visible losses tell only part of the story. Recent analysis of the true cost of fraud suggests U.S. merchants now lose $4.61 for every dollar of actual fraud when accounting for operational overhead, processing fees, and customer lifetime value erosion.
Perhaps more concerning for risk professionals is the shifting nature of the threat landscape. According to the 2024 Global eCommerce Payments & Fraud Report, refund abuse and first-party misuse where legitimate customers file illegitimate claims now rank among the most common attack vectors, affecting nearly half of all merchants surveyed. This evolution creates what fraud analysts call the false positive trap: tighten controls too much and legitimate revenue suffers or loosen them and abuse spreads.
The Emergence of AI to detect fraud
Enter agentic AI, a category of artificial intelligence that goes beyond simple decision trees or machine learning models. These systems can plan multi-step investigations and present reasoned recommendations while keeping humans firmly in control of final decisions.
Unlike traditional fraud detection systems that rely predominantly on predetermined rules or black box algorithms, agentic AI systems can explain their reasoning. These agents can adapt their investigation approach based on use cases while learning from human feedback. A McKinsey research suggests organizations are increasingly adopting agentic AI (an orchestrator) that perform research, draft actions, validate findings and escalate complex cases to human specialists. The main distinction here is, orchestration versus automation. Agentic AIs are empowering human judgment by giving analysts better tools to work faster and more consistently.
Real world Applications in Fraud Operations
The technology finds its strongest foothold in the ambiguous middle ground where traditional rule-based systems struggle most.
Coupon Abuse: Agents can analyze coupon usage patterns across linked accounts, examine device and identity relationships, test explanations such as legitimate family account sharing against suspicious arbitrage patterns. Rather than all or nothing when it comes to blocking, AI agents can recommend proportional responses ranging from warnings to account banning.
Complex Refund Claims: When customers claim items never arrived despite delivery confirmations, agents can compile comprehensive evidence packages. They pull delivery telemetry, communication histories, device patterns, and prior successful deliveries to build cases that support either goodwill credits or dispute defenses.
Chargeback Abuse: Consider a scenario where a customer claims they never received an expensive order despite order data confirming the delivery. A traditional system might automatically deny the claim or approve it based on customer tier. An agentic system, however, can investigate further. The agent examines delivery photos, compares timing with the customer’s device activity, reviews similar successful deliveries to the same address, checks for known fraud patterns in the neighborhood, etc. It then presents a nuanced recommendation, for example – Approve refund but flag payment method for monitoring on future orders.
Policy and Data Governance
Risk managers implementing agentic systems face regular risk management challenges in addition to the autonomous nature of AI behavior. Traditional validation approaches still apply such as documentation, testing, monitoring, and change control. Agentic AI requires adaptation for multi-step decision processes.
Policy governance becomes a key component. Unlike single decision models, agents operate across extended workflows where policy consistency must be maintained throughout multi-step investigations. This requires clear documentation of decision boundaries, escalation triggers, human override protocols and enforcement thresholds. Introducing a human-in-the-loop in these complex workflows improves trust and accuracy of decisions in nuanced scenarios.
Next, data governance is critical as agents would access multiple data sources during investigations. Privacy by design principles become essential, with data minimization rules that limit agents to necessary information for accurate decisions. Role-based access controls (RBAC) must extend to agent permissions, ensuring different agent types can only access appropriate data sources.
Validation processes should involve extensive testing where agents face conflicting instructions and edge cases. Introducing hypothetical scenarios that are designed to expose policy or behavioral gaps are important. In addition to above governance, Responsible AI (RAI) should clearly define roles and responsibilities for teams involved and uphold RAI principles.
Managing Agent related Risks
Agentic systems introduce risks that require guardrails against manipulation through crafted inputs or trained to ignore policy constraints through subtle prompt engineering. Effective protection requires tool-level permissions where agents must request explicit access to sensitive systems like payment vaults or customer databases. Policy adherence should be prioritized over creativity in agent training. Kill switches and rollback capabilities must be built into every agent workflow allowing immediate intervention.
In these use cases, reliability of agents matters more than creativity in fraud operations.
Measuring Success in terms of Agent performance
Traditional fraud metrics focus heavily on loss prevention i.e. dollars saved, fraud rates, false negative rates. Agentic systems require more nuanced measurement that balances fraud prevention with customer experience and operational efficiency.
Accuracy metrics should include both false positive rates (legitimate customers incorrectly flagged) and representation win rates for disputed transactions. Speed measurements should track median resolution times and time to first customer communication.
Fairness and consistency metrics become crucial for regulatory compliance and brand protection. These should monitor outcome variance across customer segments and appeal rates. Business impact metrics should capture saved revenue, incremental transaction approvals, net promoter score (NPS) and customer satisfaction score (CSAT) impacts to understand customer experience.
Privacy and Customer communication Considerations
Given the sensitive nature of fraud investigation data, privacy-by-design implementation is non-negotiable. This includes data minimization principles, secure processing environments for personally identifiable information, encryption requirements, and purpose limitation ensuring investigation data doesn’t migrate to other business uses.
Customer communication strategy becomes critical for managing false positive scenarios. Pre-written, customer-friendly explanations for common situations, additional verification requests, claim denials, account restrictions help prevent reputational damage when legitimate customers are affected by fraud controls.
Legal, Compliance and GTM teams should be involved early in implementation to ensure documentation meets regulatory requirements and communication approaches align with consumer protection regulations.
Conclusion
Agentic AI represents an evolution in fraud operations from reactive, rule-driven processes toward evidence-based, policy-guided decisioning. Agents will empower human judgment with easily available data insights and well-reasoned decisions. The implementation challenge centers around policy design, governance frameworks, privacy protection, and human-centered workflows. Organizations that invest in these foundational elements can deploy agentic systems that make customers feel understood rather than suspected while protecting revenue.
With more sophisticated fraud schemes, the customer expectations for seamless experiences continue to rise. The organization best equipped to balance protection with customer experience would have a huge market advantage.
