Use AI for Expense Fraud Detection in Your Growing Company

Growing companies often rely on the built-in features of expense management software like Expensify or Ramp. These tools are excellent for receipt capture and basic policy enforcement. Their fraud detection, however, is based on static, universal rules. They can flag an expense over $100 or a duplicate receipt, but they cannot detect contextual or patterned fraud.

A common failure scenario involves an employee submitting multiple expenses from the same vendor that are individually just under the automatic approval threshold. A system like Expensify, which checks each line item in isolation, approves all of them. A human might notice the pattern if they manually review the full report, but the finance team at a 40-person company is too busy to scrutinize every approved report. This is how thousands of dollars are lost each quarter.

The structural problem is that off-the-shelf tools use a one-size-fits-all rule engine. They lack the architecture to ingest your company's entire expense history and build a predictive model of what constitutes normal spending for your specific teams. A $300 software charge is standard for your engineering team but would be a major red flag for your HR team. Existing tools cannot make that distinction, leading to either missed fraud or excessive false positives that frustrate employees.

The first step is a data audit of your existing expense management system. Syntora connects to your platform's API to pull the last 12-24 months of approved expense reports. This historical data is the raw material for training a model that understands your company's unique financial pulse, from employee spending habits to vendor norms. This audit produces a clear picture of what patterns are present in your data.

Based on that data, Syntora builds a dedicated anomaly detection model. We use Python and scikit-learn to train a model that scores every new expense on a 0-100 risk scale. The model is deployed as a serverless function on AWS Lambda behind a FastAPI endpoint for low-cost, high-speed performance. This approach is built on our direct experience engineering financial data pipelines, like the Plaid and Stripe integrations we built for our own accounting which process transactions in under 3 seconds.

Your delivered system integrates directly into your current workflow. When an employee submits an expense, a webhook sends the data to the API. The API returns a risk score in real time. High-risk expenses are automatically flagged for manual review inside your existing expense tool or routed to a dedicated Slack channel. Your team doesn't have to learn a new dashboard. They just see more accurate alerts in the tools they already use.