Spotting Return Fraud in E-Commerce: Why Standard Rules Are No Longer Enough

Returns are normal — abuse is not

Returns are part of e-commerce. They are an expected service element and an important trust signal for many customers. Abusive returns, however, are not a normal service element — they are a risk for margin, operations and, not least, for fairness toward the customers who behave correctly.

In day-to-day operations this distinction is often blurred: every return runs through the same process, with the same rules and the same goodwill.

Blanket rules are hitting their limits

Many shops still operate with blanket rules: identical return windows, identical goodwill, identical refund logic. That is understandable — uniform rules are easy to communicate and to run — but increasingly insufficient.

Problematic return behaviour rarely shows up in a single case. It shows up in recurring patterns across multiple orders. Examples:

• conspicuous selection-style ordering with high return likelihood,
• unusually high return frequency per customer,
• recurring complaint patterns (same reason, always close to the deadline),
• targeted exploitation of lenient conditions, e.g. free returns combined with systematic multi-orders.

Standard processes are not designed to detect such patterns across multiple orders, payment methods and sessions.

From single case to pattern detection

The decisive step is moving from the individual transaction to behaviour over time. A single return is neutral. Multiple returns with a similar signature — same article categories, same complaint reasons, same shipping/billing address logic — are a signal.

The Swiss Online Retailer Survey 2025 examines for the first time which retailer data is relevant for developing tailored security solutions in commerce. That is more than operational detail: it shows that the topic is shifting from operational niche to strategic core.

What changes for retailers

For retailers this means a perspective shift. Not every return should be treated the same, and not every goodwill rule should be identical for every customer segment. Concretely:

• Trusted customers continue to benefit from simple, fast processes — automatic refunds, free returns, generous goodwill.
• For suspicious patterns, additional checks, adjusted conditions or targeted interventions become reasonable — restricted payment options, paid return shipping, manual review before refund.

The goal is not a tougher policy for everyone, but a differentiated policy for different risk profiles.

More than fraud prevention

This view is not only about fraud prevention. It touches three dimensions that are linked in practice:

1. Profitability — fewer margin-eroding, opportunistic orders.
2. Scalability — fewer manual edge cases per 1,000 orders.
3. Steerability — returns policy becomes data-driven and verifiable, not purely intuitive.

For growing shops in particular this becomes a competitive advantage: not tightening returns across the board, but intelligently distinguishing between service and risk.

What a modern approach should at least deliver

From the points above, four minimum requirements emerge:

• Structured data foundation — structured return reasons, consistent customer and order profiles, anonymised behavioural attributes.
• Pattern detection over time — anomalies across multiple orders rather than single-case evaluation.
• Differentiated decision logic — different shipping, payment and goodwill rules by risk profile.
• Fair and transparent communication — clear rules, clear reasoning, no arbitrary exceptions.

Takeaway

Standard rules will not disappear — but they are no longer enough on their own to spot return fraud while still protecting fair customers. Reducing return abuse takes more than a stricter policy. What matters is data, pattern detection and segmented decisions — as a combination, not as a single measure.