Stop Expense Fraud and Manual Reviews with a Custom AI System

Most SMBs start with tools like Expensify or Ramp. These platforms are excellent for receipt capture and basic policy enforcement, like setting a $75 meal limit. However, their logic is based on simple, static rules. A rule can block a $100 dinner but cannot identify an employee who submits four separate $70 dinners in a single week, a common way to circumvent spending caps.

Consider a 30-person company where a manager spends 5 hours per week manually approving expense reports. The company uses QuickBooks Online for accounting. An employee submits a duplicate receipt from a month ago, knowing the manager is too busy to cross-reference every old report. Another submits personal meal expenses from weekends, categorizing them as client entertainment. Rule-based systems cannot detect these context-dependent issues because they lack a memory of past transactions or an understanding of normal employee behavior.

The structural problem is that off-the-shelf expense tools are designed for universal policies, not company-specific patterns. They cannot learn what is normal for your sales team versus your engineering team. This forces finance managers into a reactive position, relying on manual spot-checks that are time-consuming and miss sophisticated fraud. The result is a slow, frustrating process that either lets non-compliant spending slip through or burdens managers with excessive administrative work.

The first step is a data audit of your last 12-24 months of expense reports. Syntora connects to your current expense platform's API or uses data exports to build a historical transaction dataset. This audit identifies your company's unique spending patterns and establishes a baseline for what constitutes a 'normal' expense for different roles and departments. You receive a report on the data's quality and its suitability for training a detection model.

Building on our experience with financial data, the technical approach would use a FastAPI service running on AWS Lambda. The service would host a machine learning model, such as an isolation forest, trained on your historical data. When a new expense is submitted, your current software sends the data to the API. The model scores the transaction in under 200ms, returning an anomaly score. This is not a simple pass or fail; it is a statistical measure of how much the expense deviates from established norms.

The delivered system integrates directly into your existing workflow. For example, expenses scoring above a certain threshold could be automatically routed to a specific manager in Expensify for manual review, while the rest are approved automatically. You receive the full Python source code in your GitHub, a runbook explaining how to retrain the model, and a dashboard to monitor its performance. The system operates for a hosting cost under $50 per month.