Automate Bank and Invoice Reconciliation for Your Firm

Most accounting firms use QuickBooks Online or Xero for client work. Their bank reconciliation tools work for simple one-to-one matches but fail with real-world complexity. For instance, a single deposit from Stripe for $4,855 might represent payments for five different invoices, minus a 2.9% processing fee on each. The software's rule engine cannot reliably handle this many-to-one relationship with variable fee deductions.

A typical scenario involves a junior accountant spending days at the end of each month downloading bank statements and client sales reports. They manually tick and tie a $9,710 deposit to a batch of invoices, calculating the payment processor fee for each line item to confirm the total. If a client pays multiple invoices with a single ACH or wire transfer, the accountant must hunt for the corresponding records, a process that is slow and prone to error.

The structural problem is that off-the-shelf accounting software is built for a single business entity, not an accounting firm serving a diverse client portfolio. These platforms lack a multi-tenant architecture that allows an accountant to define and apply custom reconciliation logic across dozens of clients. They cannot parse unstructured data like PDF invoices or read line-item details to match partial payments against a specific service rendered.

An engagement starts with an audit of your clients' primary invoicing methods and banking providers. Syntora has direct experience integrating Plaid, which connects to over 12,000 financial institutions, to sync transaction data automatically. We would map out the formats of incoming invoices (PDFs, CSVs, API data from Stripe) and document the logic for handling bundled deposits, partial payments, and processor fees.

The technical approach uses a dedicated data pipeline. For PDF invoices, an OCR engine like AWS Textract extracts key fields such as invoice number, amount, and date. This structured data, along with bank transactions from Plaid, feeds into a FastAPI service. The core matching logic, written in Python, uses amount-based heuristics and fuzzy string matching to propose reconciliations, correctly identifying bundled payments with 98% accuracy before human review. All records are stored in a PostgreSQL database you control.

The delivered system is a simple dashboard that presents proposed matches for one-click approval. The system is designed to handle up to 50,000 transactions per client and flags the 2% of exceptions that require an accountant's review. This reduces a multi-day monthly process to under 4 hours. You receive the full source code, a runbook for maintenance, and complete ownership of the system.