Split expense claims
Single expenses divided across multiple claims, dates, or categories to stay below approval or receipt thresholds.
RISK COVERAGE
Travel, Expense & Employee Spend Analytics
Stop reviewing expense reports one at a time. Test spending behaviour across every employee, every claim, and every card transaction.
Expense fraud is small, repeated, and easy to approve. A claim splits to stay under an approval threshold. A weekend purchase in the claimant's home city posts as client entertainment. A card is used at a merchant category that policy does not permit. Each item passes on its own; the pattern is what gives it away.
foretale.ai analyses travel, expense, and purchasing card data as behaviour over time rather than as isolated reports. It compares each employee against policy, against peers in the same role and grade, and against their own history.
WHAT WE DETECT
Risks tested in this domain
Personal expenses claimed as business
Retail, grocery, lifestyle, and home-city spend submitted under business categories, including weekend and holiday transactions.
Duplicate and recycled claims
The same receipt submitted twice, claimed by two employees, or reimbursed through both expense and purchasing card channels.
Corporate and purchasing card misuse
Prohibited merchant categories, cash advances, spend just below single-transaction limits, and cards active after the holder leaves.
Travel and entertainment abuse
Mileage that exceeds plausible distance, attendee counts inconsistent with amounts, and hospitality that breaches gift and entertainment policy.
Approval and policy breaches
Self-approval, approval by a subordinate, claims approved outside delegated authority, and repeated policy exceptions by the same approver.
HOW IT RUNS
Understand → Plan → Execute → Explain
foretale.ai maps your data and relationships, plans the analytics for this domain, tests the full population, and returns findings with evidence attached. Your team keeps every judgement.
DATA USED
What we need from your systems
- Expense claims, lines, and receipt metadata
- Corporate and purchasing card transactions
- Merchant category codes
- Employee master, grade, and cost centre
- Approval workflow and delegation records
- Travel bookings and itineraries
Related reading
Frequently asked questions
How do you detect split expense claims?
Split claim detection looks for a single underlying expense divided to avoid a control. The tests group transactions by claimant, merchant, and date proximity, then compare the combined total against approval and receipt thresholds. Claims that individually sit just below a limit but together exceed it are the core signal, especially when the split is repeated by the same employee.
What makes behavioural analysis better than sampling expense reports?
Sampling reviews a small fraction of claims and treats each one in isolation, so it cannot see repetition. Behavioural analysis tests every claim and compares each employee against policy, against peers in the same role, and against their own historical pattern, which is where recurring low-value abuse becomes visible.
Can you identify personal spend without reading every receipt?
Yes. Merchant category codes, merchant names, transaction timing, and location relative to the employee's home and travel itinerary are usually sufficient to rank claims by risk. Receipt review is then targeted at the highest-risk items rather than applied at random.
Does this cover purchasing cards as well as reimbursed expenses?
Yes. Purchasing card programmes are analysed with the same approach and additionally tested for split purchases, unauthorised merchant categories, limit avoidance, and cards that remain active after termination.
NEXT STEP
See it on your data.
Request a focused walkthrough—usually within one business day.