Vendor Name Normalization Using Regex Patterns and AI
Standardize vendor names across systems for accurate expense tracking and vendor analysis
The Vendor Name Chaos Problem
Look at these transaction descriptions from the same vendor:
AMAZON.COM*AB12CD34 Amazon Marketplace AMZN MKTP US*AB12CD34 Amazon Web Services AMAZON.COM PMTS AMZ*Prime Membership amazon business purchase
That's seven different variations of Amazon. Without normalization, your expense reports show seven separate vendors, making it impossible to track total Amazon spending or identify spending trends.seven different variations
Regular expressions + AI solve this by identifying patterns and consolidating variants.
Building Vendor Normalization Rules
Pattern Matching Approach
Create regex patterns that capture all vendor variations:
Amazon Pattern
Pattern: (?i)(AMZN|amazon|AMZ\*).* Matches: ✓ AMAZON.COM*AB12CD34 ✓ Amazon Marketplace ✓ AMZN MKTP US*AB12CD34 ✓ Amazon Web Services ✓ AMZ*Prime Membership Normalize to: "Amazon"Pattern: (?i)(AMZN|amazon|AMZ\*).* Matches: ✓ AMAZON.COM*AB12CD34 ✓ Amazon Marketplace ✓ AMZN MKTP US*AB12CD34 ✓ Amazon Web Services ✓ AMZ*Prime Membership Normalize to: "Amazon"Starbucks Pattern
Pattern: (?i)(starbucks|sbux|sq \*starbucks).* Matches: ✓ STARBUCKS #12345 ✓ SQ *STARBUCKS COFFEE ✓ SBUX Store 456 Normalize to: "Starbucks"Pattern: (?i)(starbucks|sbux|sq \*starbucks).* Matches: ✓ STARBUCKS #12345 ✓ SQ *STARBUCKS COFFEE ✓ SBUX Store 456 Normalize to: "Starbucks"Square Payments Pattern
Pattern: SQ \*(.+?)(?:\s+|$) Extracts vendor name after "SQ *": - SQ *COFFEE SHOP → "COFFEE SHOP" - SQ *RESTAURANT ABC → "RESTAURANT ABC"Pattern: SQ \*(.+?)(?:\s+|$) Extracts vendor name after "SQ *": - SQ *COFFEE SHOP → "COFFEE SHOP" - SQ *RESTAURANT ABC → "RESTAURANT ABC"AI-Enhanced Normalization
Combining Regex with AI Intelligence
Use regex to pre-filter, AI to make intelligent decisions:
Hybrid Approach Prompt:
"I have these vendor variations. Using the regex pattern (AMZN|amazon|AMZ).* , I've identified these as Amazon: - AMAZON.COM*AB12CD34 - AMZN MKTP US*AB12CD34 - Amazon Web Services Should all be normalized to 'Amazon', or should 'Amazon Web Services' be separate since it's a different service? Provide business logic reasoning."(AMZN|amazon|AMZ).*
Common Vendor Patterns
| Vendor | Regex Pattern | Normalized Name |
|---|---|---|
| PayPal | (?i)paypal.* | PayPal |
| Stripe | (?i)stripe.* | Stripe |
| Costco | (?i)(costco|wholesale #\d+) | Costco |
| UPS | (?i)(ups|united parcel) | UPS |
| Verizon | (?i)(verizon|vzw) | Verizon |
Handling Edge Cases
Multiple Locations
Should "Starbucks #12345" and "Starbucks #67890" be separate or combined?
Regex approach: Extract store numbersRegex approach:
Pattern: STARBUCKS #(\d+) Group 1: Store number AI decision: Keep separate if tracking by location matters, otherwise normalize to "Starbucks"Pattern: STARBUCKS #(\d+) Group 1: Store number AI decision: Keep separate if tracking by location matters, otherwise normalize to "Starbucks"Parent Companies vs Subsidiaries
AI can help determine relationships:
- Whole Foods → Amazon (subsidiary)
- Instagram Ads → Meta/Facebook
- YouTube Premium → Google
Real-World Implementation
Google Sheets Method
=IF(REGEXMATCH(A2,"(?i)amzn|amazon"), "Amazon", IF(REGEXMATCH(A2,"(?i)starbucks|sbux"), "Starbucks", IF(REGEXMATCH(A2,"(?i)paypal"), "PayPal", A2)))=IF(REGEXMATCH(A2,"(?i)amzn|amazon"), "Amazon", IF(REGEXMATCH(A2,"(?i)starbucks|sbux"), "Starbucks", IF(REGEXMATCH(A2,"(?i)paypal"), "PayPal", A2)))AI Bulk Normalization
For one-time cleanup of historical data:
"Here are 50 unique vendor name variations from my bank statements. Group them into normalized vendor names. Use these regex hints: - Anything matching AMZN|amazon → Amazon - Anything matching SQ \* → Extract name after asterisk - Anything matching PAYPAL \* → PayPal Return a mapping table."AMZN|amazonSQ \*PAYPAL \*
Best Practices
- Create a master vendor list with canonical namesCreate a master vendor list
- Build regex patterns for each canonical vendorBuild regex patterns
- Test patterns against 6 months of historical dataTest patterns
- Use AI to catch unmapped vendors and suggest patternsUse AI to catch unmapped vendors
- Review monthly for new vendor formatsReview monthly
Conclusion
Vendor name normalization is essential for accurate expense reporting and vendor analysis. By combining regex pattern matching with AI's contextual understanding, bookkeepers can automatically standardize thousands of vendor variations, saving hours of manual work while improving data quality.
