Automate Bank Reconciliation with a Custom AI Agent

Many accounting teams rely on the bank feed feature in QuickBooks Online or Xero. But these feeds break, require constant re-authentication, and do not support many regional banks or credit unions. This forces a fallback to manually uploading and keying in data from PDF statements, which is slow and introduces a 5-10% error rate from simple typos.

General-purpose OCR tools are not a solution. They extract text but lose the critical table structure of a bank statement, outputting a jumble of dates, descriptions, and numbers. You cannot reliably parse this unstructured text into clean rows for your accounting system. This leaves teams stuck with manual data entry as the only reliable, albeit painful, option.

Trying to script this with no-code platforms creates a fragile system. A common workflow involves watching a folder for new PDFs, sending them to a third-party OCR API, and then using complex text parsing rules to guess the columns. When a bank changes its statement layout, even slightly, this parsing logic fails silently. You only discover the missed transactions during an audit weeks later.

Syntora's approach to bank statement reconciliation automation begins with a discovery phase to understand your specific bank statement formats and accounting rules. We then design a dedicated API endpoint for securely uploading PDF bank statements.

The first step in the automated workflow involves an OCR service to extract all text and coordinate data from the uploaded PDF. This structured output would then be fed to a Claude API model. Syntora would prompt this model specifically for financial document layout analysis to correctly identify transaction rows, columns, and statement periods, even with variations in bank formatting.

Syntora would build the core reconciliation engine as a Python service using FastAPI. This engine would query your accounting platform's API, such as QuickBooks Online or Xero, for potential matches based on date and amount for each extracted transaction. Custom fuzzy string matching logic would be applied to description fields, allowing the system to recognize variations like "AMZN Mktp US" and "Amazon Web Services" as the same vendor. This process would significantly reduce the number of transactions requiring manual review.

For deployment, Syntora would typically use AWS Lambda for the FastAPI service, ensuring compute resources are utilized only during processing. The system would log all transactions and their match status—Matched, Needs Review, or New—to a Supabase database. This provides a detailed audit trail for every reconciliation cycle.

We prioritize clear observability in the delivered system. Structured logging with tools like structlog would provide detailed insights into agent performance. We would configure custom alerts, such as Slack notifications, if the agent's confidence score for a transaction batch falls below a configurable threshold. The final output would be a ready-to-import CSV file, or the system could be configured to write confirmed matches directly to your accounting software.