Topic starter
15/08/2026 6:13 pm
TL;DR: E-commerce platforms are being pulled between conversion and fraud control, with ghost stores, first-party abuse, and false declines all exposing the limits of IP, cookie, and rules-based detection, according to Fingerprint. The practical lesson is that buyer trust signals need to be persistent, privacy-conscious, and layered into existing fraud workflows rather than used as a friction-heavy gate.
Editorial analysis by NHI Mgmt Group, based on content published by Fingerprint: “Balancing growth & trust: Fraud risks facing e-commerce platforms in 2025”.
By the numbers:
- Regulators in Australia flagged more than 140 ghost stores in a single year.
- Global chargeback volume is expected to grow 24% from 2025 to 2028.
- Merchants reject 6% of all e-commerce orders, but between 2 and 10% of those are actually legitimate orders.
Key questions
Q: What breaks when fraud controls rely on IP addresses and cookies alone?
A: They break when legitimate users change networks, clear browser state, or travel, because the controls confuse normal behaviour for fraud.
Q: Why do persistent device signals reduce false declines in e-commerce?
A: Persistent device signals help reconnect a returning buyer across browser resets, new IP addresses, and private browsing sessions.
Q: How can security teams separate merchant fraud from first-party abuse?
A: Use different controls for seller onboarding, checkout risk, and dispute handling.
Practitioner guidance
- Tighten seller onboarding trust checks Separate merchant onboarding from checkout scoring and verify new sellers before they can generate transactional trust.
- Replace shallow signals with persistent device intelligence Use device, network, and behavioural continuity to recognise returning buyers even when cookies are cleared or IP addresses change.
- Build a distinct first-party abuse workflow Track repeat chargeback actors separately from merchant fraud and checkout fraud so dispute abuse does not get lost inside general fraud scoring.
Bottom line: Checkout fraud controls fail when they depend too heavily on shallow identifiers that legitimate users can change and fraudsters can evade.
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15/08/2026 6:19 pm
Checkout trust is becoming a decision-quality problem, not a binary fraud problem. The article shows that platforms lose money both when they let fraud through and when they block good customers. That makes signal quality, not just rule strictness, the real governance issue for fraud and identity teams. Practitioners should treat checkout trust as a measurable decision system, not a static control.
A few things that frame the scale:
- U.S. fraud losses are projected to reach $40 billion by 2027.
A question worth separating out:
Q: What governance trade-off should checkout teams manage between conversion and fraud control?
A: They need to minimise friction without making the trust model so permissive that fraud losses rise. The practical balance is risk-based decisioning, where high-confidence buyers move quickly and uncertain cases receive added review or step-up checks. That approach protects revenue while reducing unnecessary customer abandonment.
👉 Read our full editorial: Device intelligence and checkout fraud: balancing trust and conversion