Passwordless checkout and risk-based verification that reduces password resets and SMS spend without exposing stored payment details.
Password resets and SMS one-time codes are both a conversion tax and a running cost, yet removing verification entirely leaves account takeover and stored payment details exposed at checkout. Retailers need login and checkout to feel effortless for a recognised, low-risk customer while still raising the bar the moment something looks unusual.
QR-based and in-store-to-online payment flows add a further wrinkle, since they need the same underlying assurance as a web checkout but through a different interaction pattern. Achieving this without re-platforming an existing identity or checkout stack is the practical constraint most e-commerce teams operate under.
Drop-in passkeys replace password-and-OTP flows for returning customers, cutting the number of steps between arriving at the site and completing a purchase, while pre-built UI components keep integration effort manageable for the storefront team.
Rather than sending every customer a one-time code at every login, adaptive rules assess device, location and behavioural signals and reserve a challenge for the transactions that actually carry risk — a new device, a new shipping address, an unusually large order.
Passkeys and behavioural signals reduce the effectiveness of automated credential-stuffing attempts against customer accounts, which is one of the more common routes to abusing stored payment methods and loyalty balances on retail platforms.
Where checkout or top-up happens through a QR code — in-app, in-store or cross-device — the same adaptive MFA and passkey approach is applied so the transaction is verified against the account holder rather than trusted purely by possession of a scanned code.
High-volume retailers replace a portion of SMS OTP traffic with WhatsApp OTP and passkeys, lowering messaging spend and reducing the login failures that come from delayed or undelivered text messages during peak trading periods.
Removes password entry and SMS codes from the returning-customer journey while remaining phishing-resistant.
Verification is triggered by context — new device, new address, order value — rather than applied uniformly.
Adaptive MFA extends to QR-initiated payment and top-up flows across app, web and in-store touchpoints.
Recovery paths are designed so account recovery is not left as the weakest point in an otherwise strong flow.
Journey-level analytics show where challenges are triggered and how they affect completion rates.
A recognised customer on a known device opens the app and checks out with a passkey — no password, no SMS code, and no interruption to the purchase flow. The same evening, an attempt to check out on that account is made from a new device, using a shipping address that has never been used before. The adaptive rules engine recognises the change in context and requires a verification challenge before the stored payment method can be used, rather than allowing the order to proceed on the strength of a valid session alone. The retailer's fraud team can see exactly which signals triggered the challenge and adjust the sensitivity of the rule if it proves too strict or too lenient over time.
CyberLane works with e-commerce teams to identify where checkout friction and fraud exposure actually overlap, then designs adaptive rules that protect stored payment details without slowing down trusted, repeat customers. We advise on requirements, integration approach with the existing checkout and identity stack, and the business case for a phased passkey rollout, before planning a proof of concept and overseeing delivery alongside Authsignal or an implementation partner.
CyberLane is independent and works on the decision rather than the deployment. Product-specific delivery is coordinated with the vendor or a qualified implementation partner.
Capability descriptions are based on the vendor's published materials; CyberLane's wording is independently written.
We start with an independent conversation about where your exposure actually sits, before any technology decision is made.