The setting
's runs in the cloud. It covers travel policies, approvals, and bookings of hotels, flights, cars and rail.
Linked fraud signals are visible only after separate losses have become a pattern.
Earlier intervention protects corporate-card spend, while weak links can pull legitimate travelers into review.
Who you’d be doing this for
“Two travelers had to call me today because their flights were held after the card cleared last week.”
Klaus Clarke · Corporate Travel Manager · a character in this brief
She administers a managed travel program whose employees rely on corporate cards for time-sensitive bookings.
What is at stake
Corporate-card fraud cases are linked only after separate accounts have produced losses. You must decide which relationships justify a review without sweeping legitimate travelers into the queue.
Why it isn’t already fixed
Every obvious fix costs something else. That’s the part you’d have to decide.
- earlier fraud detection vs legitimate traveler clearance
- network coverage vs explainable alerts
- real-time scoring vs complete relationship evidence
- loss prevention vs analyst queue capacity
Why Navan
Corporate-card payments, onboarding, and travel bookings already meet in the same transaction flow, making relationship evidence actionable before a trip is disrupted.
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)
- Drive applied research in anomaly detection, network/graph modelling, real-time clustering, and link analysis to identify emerging fraud patterns and organized fraud rings.
- 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)
- You will lead projects from discovery through delivery, combining statistical methods, software development, and AI tools to support business decisions and reshape the traveler experience across planning, booking, and support.
- Director, AI Enablement (opens in a new tab)
- In this role, you will combine deep familiarity with modern AI tools with a product management approach, building the internal AI environments, community, and educational frameworks needed to empower every department.
- Your primary goal is to cultivate a network of AI Champions across the company who are fluent in AI and capable of building functional AI proof-of-concept (POC) solutions to solve real business challenges.
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 turns on deciding on evidence that's still thin.
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.
Potential account-takeover signals are being connected only after investigators start rebuilding the timeline.
You have to decide at which point in the Salesforce case and Workday access history the monitor stops assembling evidence and a Policy / Risk Manager takes over the risk call.
Late identity checks interrupt shoppers after they select an interest-free plan.
You must weigh earlier fraud signals against unnecessary checkout friction.
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 build controls against real harm that remain fair to the honest people who get caught in the signal.
Several Washington repair disputes lack the inspection record and written payment rationale the rule requires.
You must weigh rapid claim resolution against a control that can stand up to regulator review.
Washington drivers say they could not find a path to request an in-person inspection after photo-based damage reviews.
You must weigh a defensible remedy against reopening claims that drivers expected to be settled.
Staffing firms cannot consistently tell whether recruiter card spend is operating expense or client-funded activity.
You must weigh pilot learning against controls that remain usable for distributed recruiters.
