Blog Post
How to Automate Credit Card Reconciliation (and Ditch Excel)
If you ask any junior accountant what their least favorite task is, they will likely say: "Credit card recs." It is tedious, high-volume, and prone to messy data. But it doesn't have to be. By moving from Excel to automated credit card reconciliation, you can turn a 3-day process into a 30-minute review. Here is step-by-step how to make the switch.
Why Excel is the Bottleneck
Excel is a powerful calculator, but a terrible database. When you use it for credit cards, you face:
- Static Data: The moment you download a CSV, it's outdated. You miss late-posting transactions.
- Manual Matching: VLOOKUPs break when merchant names slightly differ (e.g., "Uber*Trip" vs "Uber*Ride").
- Security Risks: Emailing spreadsheets full of credit card data is a data privacy nightmare.
Step 1: Centralize Your Data Streams
Automation starts with connectivity. You need a tool that ingests data from two sources automatically:
- The Bank Feed: Connect your Amex/Visa corporate portal directly via API.
- The ERP/GL: Connect your accounting system (NetSuite, Sage, Xero) to pull booked expenses.
Tip: Avoid tools that require you to manually upload CSVs. That isn't true automation.
Step 2: Define "Fuzzy" Matching Rules
This is where specialized software shines. You can set up logic that says: "If the date is within +/- 3 days AND the amount matches exactly, treat it as a match."
Advanced tools like Reconwizz go further with AI matching. They learn that "Starbucks #1234" on the bank statement equals "Starbucks Coffee" in the employee expense report, eliminating the need for manual linking.
Step 3: Handle the "One-to-Many" Problem
The biggest headache in credit card reconciliation is when one payment pays off multiple transactions. For example, you pay a $5,000 credit card bill that covers 150 individual swipes.
Automated reconciliation software can automatically "explode" that $5,000 payment into its constituent line items and match them against the 150 open invoices in your GL. Doing this in Excel requires complex pivot tables; software does it instantly.
Step 4: Manage Exceptions by Exception
Once the software matches the 95% of easy transactions, your team is left with a small list of "Exceptions."
Instead of hunting for errors, your team becomes "Transaction Investigators." They focus purely on value-added work: investigating potential fraud, double-payments, or policy violations.
Ready to Ditch the Spreadsheets?
Finance teams that automate this process save an average of 20+ hours per month. That is time you can spend on analysis, not data entry.