Reduce Accounts Payable Fraud Risk with Custom AI Automation

Most small businesses run accounts payable on QuickBooks Online or Xero, often paired with a tool like Bill.com for approvals. These platforms are excellent for bookkeeping but are not designed for active fraud detection. They rely on humans to spot discrepancies. If a bad actor sends a convincing PDF invoice with a changed bank account number, these systems will process it without a warning. The approval workflow just routes the fraudulent document to a manager, who is also unlikely to catch the subtle change.

Consider this common scenario: a 25-person marketing agency receives an email from a regular freelance designer with an invoice. The freelancer's email was hacked. The invoice PDF looks identical, but the routing and account numbers are different. The finance coordinator enters the new bank details into Bill.com. The system does not flag that this is the first time this bank account has been associated with this vendor. The payment is approved and sent. The fraud is only discovered weeks later when the real designer asks about their missing payment.

This happens because off-the-shelf accounting tools operate as passive systems of record. They do not actively synthesize data from multiple sources to identify risk. They can’t see the new bank account, compare it to a list of employee bank accounts to check for internal fraud, or run a check through Plaid to see if the account is a new personal checking account instead of an established business account. The architectural limitation is that these tools are built for data entry and reporting, not real-time, cross-platform signal detection.

The engagement begins with mapping your exact accounts payable workflow. Syntora reviews how invoices are received, who enters them, what the approval chain looks like, and which systems (QuickBooks, Bill.com, email) are involved. This audit produces a data flow diagram and a list of specific fraud signals to monitor, such as a vendor's bank account changing for the first time in 12 months. This plan is approved by you before any code is written.

Based on that map, a verification system is built. For invoice processing, the system would use a Python service running on AWS Lambda to extract data from incoming PDFs. That data is then cross-referenced with your vendor master file, which could be stored in a Supabase PostgreSQL database for fast lookups. The core logic, written in FastAPI, checks for anomalies: Does the bank account on the invoice match the one on file? Is this invoice amount more than two standard deviations higher than the vendor's average? Is this a brand-new vendor?

The delivered system integrates directly into your existing process. When a new invoice arrives, it first passes through the verification API. If it is low-risk, it proceeds to your normal approval workflow. If the system flags it with a risk score over 80, it holds the invoice and sends a detailed alert to your finance team in Slack or by email. This allows your team to focus their manual effort only on the 5% of invoices that are actually suspicious, rather than being overwhelmed by volume.