Data Cleaning with Regex Before AI Analysis for Bookkeepers

Why Data Cleaning Comes First

The old programming adage "garbage in, garbage out" applies doubly to AI-assisted bookkeeping. AI models work best with clean, structured data. Feed them messy, inconsistent data, and you'll get unreliable results—no matter how advanced the AI.

Regular expressions are the bookkeeper's power tool for data cleaning. Before sending your financial data to an LLM for analysis, use regex to remove noise, standardize formats, and ensure quality.

Common Data Cleaning Tasks

1. Remove Extra Whitespace

Bank exports often have irregular spacing:

2. Standardize Currency Symbols

Before: "USD 100.00", "US$ 100.00", "100.00 USD"
Pattern: (USD\s*\$?|US\$|\s*USD$)
Replace: "$"
After: "$100.00"

Makes all amounts consistent for AI processing

3. Remove Non-Printable Characters

Pattern: [^\x20-\x7E]+
Replace: ""

Removes: Tabs, line breaks, special characters
Leaves: Only printable ASCII characters

Critical for clean CSV imports

4. Normalize Account Numbers

Before: "Account #1234", "ACCT 1234", "Acct# 1234"
Pattern: (?i)acct\.?\s*#?\s*(\d+)
Extract: Group 1 (just the number)
After: "1234"

Standardized for matching and lookups

Pre-Processing for AI Analysis

Clean Transaction Descriptions

Bank transaction descriptions contain clutter that confuses AI:

Standardizing Field Formats

Phone Numbers

Before: "(760) 249-7680", "760-249-7680", "7602497680"
Pattern: \(?(\d{3})\)?[-.\s]?(\d{3})[-.\s]?(\d{4})
Replace: "$1-$2-$3"
After: "760-249-7680"

Consistent format for AI to process contact info

Email Addresses

Extract valid emails:
Pattern: [a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}

Validate: Use AI to check if domain exists
Clean: Convert to lowercase for consistency

EINs (Employer Identification Numbers)

Before: "12-3456789", "123456789", "12 3456789"
Pattern: (\d{2})[-\s]?(\d{7})
Replace: "$1-$2"
After: "12-3456789"

Properly formatted for IRS forms

Removing Duplicates

Duplicate Transaction Detection

Use regex to create unique identifiers:

Create hash from:
- Date: \d{2}/\d{2}/\d{4}
- Vendor: ^[A-Z\s]+
- Amount: \$[\d,]+\.\d{2}

Combine: "2025-11-15|AMAZON|1234.56"

AI can then: "Find all transactions with identical hashes.
These are likely duplicates. Flag for review."

Special Characters and Encoding

Remove Problem Characters

// Smart quotes to straight quotes
Pattern: [""]
Replace: "

// Em dash to hyphen
Pattern: —
Replace: -

// Degree symbol to word
Pattern: °
Replace: deg

// Non-breaking space to regular space
Pattern: \u00A0
Replace: " "

Google Sheets Cleaning Functions

Comprehensive Cleaning Formula

=TRIM(
  REGEXREPLACE(
    REGEXREPLACE(
      REGEXREPLACE(A2, 
        "[^\x20-\x7E]", ""       // Remove non-printable
      ), 
      "\s+", " "                  // Multiple spaces to one
    ),
    "^\s+|\s+$", ""              // Trim edges
  )
)

Chains three regex operations:
1. Remove special characters
2. Collapse multiple spaces
3. Trim whitespace

AI-Guided Data Quality Checks

After regex cleaning, use AI to verify quality:

Real-World Cleaning Workflow

Excel/CSV Import Preparation

1. Export from bank (often messy format)
2. Regex cleaning:
   • Remove extra spaces: \s+ → " "
   • Standardize amounts: Add $ and .00 where missing
   • Fix dates: Convert all to YYYY-MM-DD
   • Clean vendor names: Remove transaction codes
3. AI validation: "Check this cleaned data for any remaining issues"
4. Import to QuickBooks with confidence

Best Practices

1. Clean early: Don't wait until reconciliation time
2. Document patterns: Save regex for reuse
3. Test thoroughly: Run on historical data first
4. Validate with AI: Double-check cleaning didn't corrupt data
5. Keep originals: Always maintain raw data backup

Conclusion

Data cleaning is the unglamorous but essential foundation of AI-assisted bookkeeping. By using regex to systematically clean and standardize your financial data before AI analysis, you ensure accurate results, save debugging time, and build reliable automated workflows.

Remember: Clean data in = accurate insights out!