Travel & Hospitality

Seat spinning is the automated holding of airline seats without completing the booking, so the inventory reads as unavailable to real travelers until each hold expires. The same pattern hits hotel rooms, and bots can renew a hold as soon as it lapses, keeping inventory off sale for longer.

CHEQ helps at the point where the hold is created. It classifies the entity behind each booking session before inventory is reserved, so a hold started by automation can be treated differently from one started by a traveler.

That classification draws on three kinds of evidence:

• What the entity is, whether a person, a bot, or an AI agent, and who operates it
• How the booking flow is used, which reflects the session’s behavior rather than only what was selected
• Whether the details entered hold up, including user data validation on the email addresses and phone numbers given at booking

Your team then sets the response. A suspected seat spinner can be throttled or misdirected, while travelers moving through the same flow continue without a challenge.

CHEQ classifies each entity requesting fares and availability, so an authorized partner and an unauthorized scraper can receive different responses even though both are automated.

In travel, the line that matters is authorization rather than automation. Authorized OTAs and approved AI agents resell or research your inventory with your agreement. Unauthorized resellers and competitors collect the same fares to undercut you, and their search volume slows the pages real travelers use.

Enforcement is proportional, so each group can get its own answer. An unauthorized scraper can be throttled or misdirected, an approved partner can keep full access, and an unfamiliar requester can be monitored until its purpose is clearer.

Your team sets each of those policies. CHEQ supplies the classification and the evidence behind it, so a partner who asks why its access changed can be shown the sessions involved.

CHEQ helps at login and again at redemption, the two points where a taken-over loyalty account turns into a loss.

Points and miles can be spent like stored value, so attackers work toward them in stages:

• At login, breached password lists from other travel brands are replayed against your member login page
• Inside the account, a working session changes contact details or adds a new traveler name
• At redemption, points are spent on award flights, stays, or partner rewards booked for someone else

CHEQ classifies the entity and assesses identity signals at each stage rather than only once. This is continuous affirmation: trust is reassessed at higher-risk transitions like redemption, not settled at the login screen.

That supports proportional responses, such as a step-up before a redemption instead of a block at login, so a member who mistypes a password is not locked out of their own points.

CHEQ classifies AI agents as their own entity type, so a travel brand can decide what an agent may do instead of treating every agent as a scraper.

This matters because an agent often has a real buyer behind it. Blocking an AI assistant that is comparing vacation packages for a customer can mean losing that customer’s booking.

CHEQ separates agents by what their behavior suggests they came to do:

• An assistant reviewing a reasonable number of options for one trip, which usually warrants access to search and fare pages
• An agent moving into seat selection or payment, where a travel brand may want stronger evidence of the traveler behind it
• Anonymous automation collecting an entire pricing database, which calls for a different policy altogether

Intent here is an informed inference from behavioral signals, not a certainty about any single agent. The classification comes from CHEQ, and the decision about what each agent may do stays with your team.

Yes. CHEQ classifies the session behind each booking at the payment page, so risk is assessed before the booking completes rather than after a dispute arrives.

Timing matters more in travel than in most categories. A chargeback on a flight or hotel booking can arrive after the trip has been taken, when the seat or room can no longer be sold again.

Travel payment fraud tends to follow a few recognizable patterns:

• Card testing, where stolen card numbers are checked through small purchases before a larger booking
• Bookings through taken-over accounts, which use a member’s stored card or points
• Bookings resold to third parties, where a stolen card pays for travel in another person’s name

CHEQ passes its verdict to the payment and fraud tools you already run, which keep the final decision on each booking.

CHEQ’s classifications are designed to move into the tools travel teams already use, so privacy, marketing, and fraud teams each work from the same verdict.

Travel brands already use CHEQ in more than one of those roles:

• Privacy and consent: All Nippon Airways uses CHEQ Enforce to manage its consent banner and tags across a site visited by millions of people around the world
• Paid media: European Camping Group blocked more than 2 million invalid users in one year with CHEQ Acquisition
• Security and fraud: classifications reach infrastructure through CloudFront and Cloudflare, and export through S3 and a retrospective data API

On the marketing side, CHEQ supports IP blocking in Google Ads and audience exclusion across platforms including Meta, Bing, YouTube, and TikTok. That keeps invalid visitors out of the audiences travel campaigns spend against.