Improve Cash Flow and Reduce Expense Fraud with AI

Financial services firms often rely on expense management platforms like Ramp or Expensify. These tools are effective for receipt capture and basic policy enforcement but fail at contextual risk analysis. A rule to flag expenses over $500 cannot distinguish between a legitimate client dinner and a fraudulent charge from a shell company with a similar name. The systems lack the intelligence to analyze vendor history, expense frequency, or cross-employee spending patterns.

Consider a 20-person advisory firm where an employee submits a $4,500 expense for a 'conference sponsorship'. The accounts payable clerk sees a matching invoice, and the department head approves it via a one-click email. Standard tools would not flag that the vendor LLC was created last month, this is the first transaction with them, and the amount is 5x the typical sponsorship cost. The approval workflow is a rubber stamp because the human approvers lack the data to make an informed decision.

The structural problem is that off-the-shelf tools use a rigid, one-size-fits-all data model. They cannot ingest external signals, like checking a new vendor against a business registry or correlating travel expenses with CRM activity. Your firm's unique spending DNA and risk profile cannot be encoded into their simple rule engines. You are forced into a manual review process that only catches the most obvious violations.

This manual oversight creates a 2-3 day lag in closing the books, making cash flow reports perpetually out of date. Decisions are based on old data. More importantly, subtle, coordinated fraud can go undetected for months because no single person has a complete view of all transactions in real time. The risk accumulates silently until a major loss occurs.

Syntora begins with an audit of your entire expense lifecycle, from card swipe to general ledger entry. We map every data source (Plaid for bank accounts, card provider APIs) and every step in your approval chain. This discovery phase produces a specific technical plan for a system tailored to your firm's spending patterns and risk tolerance.

We built a core system for our own use that connects Plaid and Stripe to a PostgreSQL ledger, a pattern that we would extend for your needs. A FastAPI service would ingest real-time transaction streams and feed them into a custom anomaly detection model. Using a library like scikit-learn, the model learns your firm's normal spending behavior to score new expenses for risk. This entire system can be hosted on AWS Lambda, keeping infrastructure costs under $50/month for a 20-person team.

The delivered system is not another dashboard to check. It's an API that integrates into your existing workflow. A high-risk expense can trigger a priority alert in a dedicated Slack channel with a plain-English explanation of why it was flagged. This allows your finance team to shift from tedious manual checking to efficient exception handling. The system processes transactions in under 500ms, closing the gap between spend and visibility.