Custom AI for Bank Statement and Invoice Reconciliation

Most accounting firms rely on the bank rules in QuickBooks Online or Xero. These tools work for simple, recurring transactions but fail with ambiguity. Their matching logic depends on exact string matching, so a payment from a client appearing as "ACME CORP PAYMENT" one month and "STRIPE*ACME" the next requires two separate, fragile rules that often break.

A typical scenario involves an accountant managing 15 SMB clients. At month-end, they download CSVs from 20 different bank accounts. A deposit of $5,432.10 labeled "ACH Credit" arrives. The accountant must now manually search client invoices, emails, and payment portals, trying to find a matching invoice. This single transaction can take 15 minutes to resolve, accumulating to dozens of hours of low-value work every month.

The structural problem is that off-the-shelf accounting platforms are closed ecosystems designed for mass-market use. You cannot inject a custom AI model to perform probabilistic matching, for example, to determine a 95% likelihood that a transaction matches an invoice based on amount, date proximity, and past payment behavior. You are limited to the platform's rigid, rule-based logic.

The result is that skilled accountants spend their time on data-entry tasks instead of high-value advisory work. This caps the number of clients a firm can profitably manage and forces them to hire junior staff for manual reconciliation, which increases overhead and the potential for errors.

The project would begin with an audit of your current reconciliation process. Syntora reviews examples of your bank statements and a sample of 100+ invoices across several clients. This work identifies common patterns, edge cases, and the data fields available for matching. You receive a mapping document showing exactly which data points will be used before any code is written.

The core system would be a Python service using the Claude API to extract structured data from PDF and email invoices. For bank data, we used Plaid in our own internal system for direct API access. The engine would match transactions to invoices using a multi-factor scoring model, considering amount, date (within a 5-day window), and fuzzy text matching on customer names. This process runs daily on AWS Lambda, costing under $50 per month for thousands of transactions.

The delivered system is a simple dashboard showing three categories: auto-reconciled matches with over 90% confidence, suggested matches for review, and exceptions requiring intervention. For our own accounting system, we built a 12-tab admin dashboard with Express.js. For a client firm, the deliverable is a focused tool that plugs into your existing workflow, reducing manual work by over 80%.