Detect Fraudulent Expenses with a Custom AI System

Most small businesses use tools like QuickBooks Online or Expensify for expense management. These systems rely on manually configured rules, such as flagging any transaction over $500. This approach cannot detect a pattern of ten fraudulent $499 transactions from the same employee, as each one falls below the threshold. The logic is rigid and lacks the context of historical behavior.

Newer platforms like Ramp offer more controls but are limited to their own corporate card ecosystem. The platform is blind to fraudulent reimbursements paid from a company bank account or suspicious activity on other payment platforms. If an employee submits an out-of-pocket expense claim, Ramp has no data to cross-reference its validity against other spending, creating a significant blind spot.

Consider a 20-person firm where an employee submits five separate expense reports for "client entertainment" under the $100 auto-approval limit, all on the same day. A rule-based system approves each one because they are evaluated independently. An AI-based system would see this as a high-frequency anomaly from a single user in a short time frame and flag it for manual review. The core problem is that off-the-shelf tools treat each expense as a discrete event. They are not architected to analyze transaction sequences, vendor frequency, or spending velocity across your entire financial footprint.

The first step is a data audit of your existing financial stack. Syntora connects to your bank accounts via Plaid, your payment processors like Stripe, and any existing expense software. This process maps every source of spend into a single, unified view. Syntora has built these exact integrations before, creating a custom PostgreSQL ledger that provided automated transaction categorization and real-time balance tracking.

For anomaly detection, this unified data feed is used to train a model. We would use Python with scikit-learn to build an Isolation Forest model, which is highly effective at identifying outliers in financial data. The model runs on a schedule as an AWS Lambda function, scoring each new transaction. Transactions with a high anomaly score trigger an alert with a detailed explanation, delivered directly to you via Slack or email. The total hosting cost for this is typically under $25 per month.

The delivered system is a set of serverless functions and a database that you own completely. It operates in the background, pulling new transactions, scoring them in near real-time, and pushing actionable alerts into your existing workflow. Based on our past financial integration work, a bank sync takes under 3 seconds, and the added anomaly scoring would add less than 500ms of processing time. The standard build for this system is a 3-week engagement.