The setting
's runs in the cloud. It covers travel policies, approvals, and bookings of hotels, flights, cars and rail.
Hotel deposits are being treated like risky new-card activity after the booking behavior changed.
Fast hotel confirmation protects traveler plans, while payment controls still need to catch emerging card fraud.
Who you’d be doing this for
“My travelers have a confirmation, then they’re asking why the hotel still hasn’t been paid.”
Navin Joshi · Corporate Travel Manager · a character in this brief
Manages a global travel program whose travelers rely on corporate cards for booked hotel stays.
What is at stake
Corporate-card hotel deposits now send 8.4% of legitimate payments to review. You must lower the holds without opening a new fraud-loss path.
Why it isn’t already fixed
Every obvious fix costs something else. That’s the part you’d have to decide.
- hotel confirmation speed vs fraud-loss protection
- narrower thresholds vs emerging-pattern coverage
- queue relief vs model reliability
- booking signals vs payment-event timing
Why Navan
Corporate-card hotel deposits sit directly between a managed booking confirmation and a traveler’s ability to keep the reservation.
Written with these in mind
Not your kind of problem? 61 more at Navan, or browse every organization.
Where the organization context comes fromThe postings this brief was written from, and the reporting read before it.
The organization context comes from public information. The situation and characters are written for the brief.
Job postings it was written from
Public descriptions of the work at Navan. They are listed as sources, not as openings. The lines under a posting are its own words, where it meets this brief.
- Senior Data Scientist, Fraud Operations (opens in a new tab)
- Lead the design and deployment of advanced ML features, build rule workflows to detect and prevent fraud across travel and expense.
- Partner cross-functionally with other Risk Strategy team members, Fraud operations, Engineering, and Product teams to translate insights into scalable prevention rules, thresholds, and model-driven interventions.
- Sr. Data Scientist (opens in a new tab)
- Build reusable data-science solutions in Python and SQL, ranging from exploratory analysis to tested analytical workflows developed alongside Engineering.
- Director, AI Enablement (opens in a new tab)
This is the setup. The work is inside.
Running it puts you in the room: the full situation and its constraints, stakeholders who push back in their own words, and the decisions that are yours to make. What you produce becomes a Day One Plan — work you can show someone instead of describing.
Where similar work appears
Other live briefs connected to this one, and what connects them.
Similar problems at other organizations
Like this one, each of these is about money moving incorrectly, late, or twice.
Losses have risen 2.6-fold from synthetic identities that pass today's onboarding rules, and a blunt fix would reject real founders.
You set the thresholds and the review evidence before a cutover date that is already fixed.
Payment-method blocks rose 62% in one week, and 73% of sampled cases were legitimate riders.
You have to weigh faster rider recovery against keeping the abuse path contained.
Reliable card customers are hitting vendor-payment blocks after temporary cash dips.
You must widen payment speed for the right cohort without admitting persistent repayment risk.
The same setting, from another role
Each is set around Navan’s Corporate Travel Management Platform, written for a different role. They’re separate briefs, not the same scenario.
Hotel complaints are scattered across conversations, evaluation results, and bug bashes, obscuring recurring booking failures.
You have to decide where clustering of hotel evidence hands over to the PMs who frame the problem and choose what enters metric review.
Travelers abandon connected trips when they must restate their needs for each booking leg.
You must weigh a simpler planning experience against clear policy and booking boundaries.
Confirmed hotel stays are being reclassified after travelers receive booking confirmation.
You must weigh a durable audit record against a recovery path that keeps travelers housed.
Other problems with a similar purpose
In each of these, as in this one, the aim is to make complex payment systems feel dependable in the exact real-world moment someone needs to complete one.
Some New York refunds miss the lender statement window, leaving shopper balances open after a merchant approves the return.
You have to weigh a simpler merchant handoff against controls that prove every refund record arrived on time.
Approved agent purchases with changed prices are failing during authentication.
You have to weigh faster completion against a control path that bad actors could exploit.
Mobile job completion is creating duplicate charges after some technicians reconnect.
You must protect payment integrity without making field completion brittle.
