Stop Employee Expense Fraud with a Custom AI System

Most small businesses rely on tools like Expensify for expense management. These tools are effective for scanning receipts and enforcing simple rules, like a $75 meal limit. The system's weakness is its lack of memory or context. It can't detect a legitimate $60 receipt submitted twice, one month apart, because each submission passes the static rule check independently. The platform leaves this kind of duplicate fraud detection to a manager's manual spot-check during a busy month-end close.

Newer platforms like Brex or Rippling connect directly to the corporate card feed, which solves part of the problem. However, they struggle with expenses that have no corresponding transaction, like mileage claims. An employee can submit a Google Maps screenshot showing a 50-mile trip to a client they never visited. The system approves the reimbursement because the submitted document appears valid, but it has no ground-truth data to verify the trip against an actual calendar event or vehicle log.

Consider a 30-person company. An employee submits a $40 expense for a taxi with a valid receipt. Two weeks later, they submit the same receipt again. Because both claims are under the spending limit and have a valid receipt image, the software approves both. The finance manager, reviewing 200 reports that month, misses the duplicate. The expense is paid twice. This happens because the system's architecture is built for individual transaction approval, not for stateful analysis of an employee's spending patterns over time.

The structural problem is that off-the-shelf tools provide a one-size-fits-all rule engine. They cannot incorporate your company's unique data, such as calendar entries or project codes, to validate the business purpose of an expense. This forces your finance team into a reactive, manual audit role instead of preventing fraud proactively.

The first step is mapping your expense policy rules to your data sources. Syntora connects to your corporate card provider (Stripe, Amex), bank feeds, and accounting software (QuickBooks, Xero) to create a single source of truth for all spending. We have built these types of integrations before, connecting Plaid bank data to a custom PostgreSQL ledger for real-time balance tracking. You receive a data flow diagram showing exactly how expenses will be validated against transactions before the build begins.

The technical solution is a FastAPI service that ingests expense reports via API or webhook. For each submitted receipt, the Claude API extracts the merchant, date, and amount. This data is then checked against the PostgreSQL transaction ledger to find a matching card swipe. The system flags any submission without a corresponding transaction, any potential duplicate based on merchant and amount within a 45-day window, and any expenses dated on a weekend. The logic is written in Python, enabling complex checks that simple rule engines can't perform.

The delivered system is a set of AWS Lambda functions that run automatically. When a fraudulent or out-of-policy expense is detected, a notification is sent to a designated Slack channel with a detailed explanation (e.g., "Flagged: Duplicate charge from Starbucks on 2023-10-26. Matches existing expense #5821."). The audit itself completes in under 10 seconds. You receive the full source code, a deployment runbook, and full ownership of the system.