Automate Bank Reconciliation with a Custom AI Agent
AI agents use optical character recognition (OCR) to extract transaction data from bank statement PDFs. They then match these transactions against your accounting software entries using custom-built logic.
This system is for teams that process PDF statements from multiple banks or have transaction rules too complex for standard bank feeds. The build scope depends on the number of unique statement formats and the complexity of the matching rules needed to assign transactions to the correct general ledger accounts.
We built a reconciliation agent for a 7-person services firm reconciling 1,200 transactions monthly from 4 different banks. Their manual process took 10 hours per month. The AI agent cut this down to under 30 minutes of final review, allowing them to close their books two days earlier.
What Problem Does This Solve?
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.
How Does It Work?
We build a dedicated API endpoint that accepts PDF bank statements via a secure upload. First, the PDF is processed by an OCR service to extract all text and coordinate data. We feed this structured output to a Claude API model that has been prompted specifically for financial document layout analysis. This correctly identifies transaction rows, columns, and statement periods, even if the bank's formatting changes month-to-month.
The core reconciliation engine is a Python service built with FastAPI. For each extracted transaction, it queries your accounting platform's API (like QuickBooks Online or Xero) for potential matches based on date and amount. It then applies fuzzy string matching and custom logic to the description field, correctly matching vendor names like "AMZN Mktp US" and "Amazon Web Services" to the same vendor record. This engine reduces a list of 1,200 transactions down to the 30-40 that require human review.
We deploy the FastAPI service on AWS Lambda, so you only pay for compute time when a statement is being processed. A new PDF upload triggers the function, which completes its entire workflow in under 8 seconds. All transactions, including their match status (Matched, Needs Review, New), are logged to a Supabase database. This provides a full audit trail, and hosting typically costs less than $25 per month.
We use structlog for detailed, structured logging. If the agent's average confidence score for a batch of transactions drops below 90%, it triggers a Slack alert for your team. The final output is a ready-to-import CSV file, or the agent can write confirmed matches directly back to your accounting software. The entire process from PDF upload to a ready-to-review report takes less than two minutes.
What Are the Key Benefits?
Close Your Books in Hours, Not Days
Automated processing reduces reconciliation from over 10 hours of manual data entry to under 30 minutes of final review.
A Fixed Build Cost, Not a Per-User Fee
Pay once for the system. After launch, you only cover minimal cloud hosting costs, not a recurring software subscription.
You Own The Reconciliation Engine
We deliver the complete Python source code to your GitHub repo. You are never locked into our service and can have any developer extend it.
Alerts When a Bank Changes Its Format
The system monitors match confidence. If a new PDF layout causes errors, you get an immediate alert instead of finding the problem at month-end.
Connects Directly to Your Ledger
The agent integrates with QuickBooks Online, Xero, and other platforms via their APIs, writing matched transactions back automatically.
What Does the Process Look Like?
Discovery & Statement Analysis (Week 1)
You provide 3-5 sample bank statement PDFs from each financial institution. We analyze the layouts and map the fields needed for your general ledger.
AI Agent Development (Week 2)
We build the core OCR and matching logic in Python. You receive a demo showing the agent processing your sample statements and flagging exceptions.
Integration & Deployment (Week 3)
We connect the agent to your accounting software API and deploy it to your AWS account. You receive credentials to the review interface.
Parallel Run & Handoff (Week 4)
You run the agent alongside your manual process for one month-end close. We tune the logic, then deliver the final code, documentation, and runbook.
Frequently Asked Questions
- How much does a custom reconciliation agent cost?
- The cost and timeline depend on the number of unique bank statement formats and the complexity of your matching rules. A project with two banks and standard transaction types is a 2-week build. One with five banks and multi-currency transactions takes closer to four weeks. We provide a fixed-price quote after the discovery call.
- What happens if the AI miscategorizes a transaction?
- The agent assigns a confidence score to every match. Anything below a 95% threshold is flagged for human review in a simple dashboard. It never silently commits uncertain data to your books. This ensures an accountant makes the final call on ambiguous entries, preventing errors from propagating into your financial reports.
- How is this better than an off-the-shelf tool like DocuParser?
- DocuParser uses rigid templates that you build with a point-and-click interface. If a bank changes the PDF layout, your template breaks. Our AI agent adapts to layout changes because it understands the context of a financial document. It actively looks for 'Date' and 'Amount' columns, rather than just data in a fixed position.
- How do you handle the security of our financial data?
- The system is deployed entirely within your own cloud environment on AWS. We never have access to your production data after the initial handoff. All data in transit is encrypted with TLS 1.2, and we use AWS Secrets Manager to handle API keys and credentials, ensuring they are never hardcoded in the source code.
- Does this only work for bank statements?
- No. The core Claude API-based technology can be adapted for credit card statements, broker reports, and even vendor invoices. We can build a single agent that handles multiple document types, routing them to the correct parsing and reconciliation logic based on the file's content and structure.
- What is the typical accuracy rate we can expect?
- For standard bank statements from major institutions, we achieve over 99% accuracy on data extraction. The transaction matching accuracy depends on your chart of accounts and vendor data. Typically, 80-90% of transactions are matched automatically with high confidence. The remaining 10-20% are surfaced for a quick manual review.
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