Expensya payment cards with AI-powered anomaly detection and fraud compliance
The problem
Companies lacked upfront control over employee spending, relying on post-purchase reconciliation, cash advances, and out-of-pocket reimbursements, with no enforcement of spending policies across geographies.
Workflow diagram · grounded in source
1
Set card rules and limits
Trigger
Administrators set payment limits, expense categories, budgets, and the days and hours that cards will work for employees.
▾ source quote
“Set the payment limits, expense categories, budgets, and the days and hours that the cards will work for your employees”
2
Rules checked at payment
Validation
These rules are checked each time the payment card is used.
▾ source quote
“These rules are checked each time the payment card is used”
“AI-powered anomaly detection to spot out-of-the-ordinary expense amounts, identify unusual spending patterns, and validate receipts”
4
Freeze or disable card
Human review
With real-time visibility to all payments, the card or budget can be disabled immediately for concerns of fraudulent activities.
▾ source quote
“With real-time visibility to all payments, you can disable the card or the budget immediately for concerns of fraudulent activities”
5
Native reconciliation
Integration
Cards, expenses, budgets, and travel bookings are all in one platform with native reconciliation between bookings, payments, and expenses.
▾ source quote
“cards, expenses, budgets, and travel bookings are all in one platform, you also have native reconciliation between the bookings/payments and the expense with a unified view for payments and expenses in one platform”
Reported outcome
Employees literally cannot spend out of policy, and AI-powered fraud detection keeps compliance always audit-ready with full spend traceability.
Reported metrics
Card transaction rebateup to 0.5%
Reported stack
ExpensyaApple PayGoogle PayAdyenERP
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