Retailers have never had more powerful tools to accelerate online growth. Today’s eCommerce platforms make it remarkably easy to launch storefronts, acquire new customers and process massive transaction volumes at lightning speed.
But this exact convenience has introduced a systemic and costly vulnerability. Fraud losses increasingly outpace revenue. Currently, 62% of retail organizations rely primarily on the rudimentary fraud protections built directly into their eCommerce platforms. On the surface, these platform-native tools promise speed and simplicity. They allow retailers to deploy quickly without wrestling with complex risk infrastructure.
However, the data reveals a striking contradiction: among retailers relying on these default controls, nearly half report that their fraud losses are growing significantly faster than their revenue. To scale securely, merchants must look beyond built-in commerce tools. Platform native engines are designed to facilitate sales and reduce friction. They fundamentally fail to defend against coordinated, AI-backed fraud ecosystems.
The Checkout Trap: Looking in the Exact Wrong Place
Retailers operate under intense pressure to maintain frictionless checkouts. In eCommerce, adding even a fraction of a second of friction quickly translates into abandoned carts and lost revenue. Fraudsters understand this dynamic perfectly and exploit the tension between sales and security.
The most damaging retail fraud types do not originate at the point of payment. They begin much earlier in the customer journey, hiding within routine account activity, login behaviors and promotional usage. By the time these sophisticated attacks surface as disputed transactions or chargebacks, the underlying compromise is already complete.
- Account Takeover (26% of losses): Attackers bypass weak authentication to hijack legitimate, trusted profiles — often weeks before those accounts are ever weaponized for fraudulent purchases.
- Promotion Abuse (18% of losses): Bad actors orchestrate automated networks of synthetic identities to systematically drain new-customer marketing budgets and loyalty rewards at scale.
Because platform-native tools focus almost exclusively on transaction-level signals at the moment of checkout, they have virtually no visibility into these highly damaging account-level anomalies. They are looking in the exact wrong place, trapping retailers in a costly cycle of playing catch-up.
The AI and Headcount Paradox
Unlike the financial sector’s highly centralized risk teams, retail fraud responsibility is deeply fractured. Ownership is often split across eCommerce, payments, customer operations and finance departments. These siloed teams juggle conflicting priorities and operate with incomplete visibility, making cross-functional coordination incredibly difficult.
To bridge this operational gap, the retail sector has fully embraced automation —97% of retail organizations actively use AI in their fraud workflows. Yet, ironically, the fraud function is expanding rather than shrinking. An identical 97% plan to increase fraud headcount this year, with nearly 60% preparing to add three or more full-time risk professionals.
Why is automation driving headcount? Because effective AI surfaces subtle behavioral outliers and cross-channel patterns that manual reviews would never catch. However, when these advanced signals are generated within disconnected point solutions, they trigger complex, time-consuming investigations. Without a unified data architecture, adding AI simply increases the demand for skilled analysts to interpret what the models find.
What You Will Discover in the 2026 Report
Closing the detection gap requires moving defenses upstream and treating automation as a capability multiplier, not just an alert generator. The 2026 Retail Fraud Guide explores how 330 leading merchants are mastering AI adoption and scaling securely without compromising the customer experience.
Download this comprehensive report to learn about how to:
- Expand the Frame
Shift detection upstream to account creation, login behavior and device intelligence to neutralize fraud long before a transaction occurs. - Measure the Right Thing
Look beyond standard chargeback rates to identify behavioral anomalies, promotion usage patterns and genuine account-level exposure. - Treat AI as a Multiplier
Build your machine learning models on a unified view of the entire customer lifecycle, ensuring automation builds long-term capability rather than acting as a standalone feature. - Align Cross-Functional Teams: Break down internal data silos between eCommerce, payments and risk teams to execute a cohesive, lifecycle-wide defense strategy.
