Automated Bank Reconciliation Using Regex and AI

The Reconciliation Time Sink

Bank reconciliation is essential but time-consuming. Traditional reconciliation of 200 monthly transactions can take 4-6 hours. Bookkeepers must manually match each bank transaction to general ledger entries, identify discrepancies, and investigate variances.

With regex pattern matching and AI assistance, you can reduce this to 30-45 minutes while actually improving accuracy.

The Regex + AI Reconciliation Method

Phase 1: Data Preparation

Use regex to standardize both data sources:

// Bank statement cleaning
1. Extract amounts: \$[\d,]+\.\d{2}
2. Extract dates: \d{2}/\d{2}/\d{4}
3. Clean descriptions: Remove reference numbers, codes

// General ledger cleaning
1. Standardize GL amount format
2. Normalize GL dates to same format
3. Extract check numbers: CHECK #(\d+)

Phase 2: Exact Matching with Regex

Create composite keys for automatic matching:

Match Key Pattern:
Date + Amount + First word of description

Regex: (\d{4}-\d{2}-\d{2})\s+\$([\d,]+\.\d{2})\s+([A-Z]+)

Example:
Bank: "2025-11-15 $500.00 AMAZON PURCHASE"
GL:   "2025-11-15 $500.00 AMAZON"
Match Key: "2025-11-15|500.00|AMAZON"

Result: ✓ Exact Match

Phase 3: Fuzzy Matching with AI

For non-exact matches, use AI:

Identifying Unmatched Items

Bank Items Not in GL

After regex matching, remaining items are unmatched.

AI Prompt:
"These bank transactions have no GL entry:

1. 2025-11-10 | $45.00 | BANK FEE
2. 2025-11-15 | $1,200.00 | DEPOSIT XYZ CORP

For each:
- Suggest likely GL category
- Identify if it's a missing entry or legitimate unrecorded item
- Recommend correcting action"

GL Items Not in Bank

GL entries without bank matches might be:
- Checks not yet cleared (pattern: CHECK #\d+)
- Pending deposits
- Accruals (pattern: ACCRUAL|PREPAID)
- Errors

AI analyzes context to determine which

Automated Reconciliation Workflow

1. Extract bank data

   Use regex to pull date, amount, description

2. Extract GL data

   Same fields from accounting system

3. Standardize formats

   Regex patterns ensure apples-to-apples comparison

4. Exact match (regex)

   ~70-80% match automatically

5. Fuzzy match (AI)

   ~15-20% matched with AI intelligence

6. Flag exceptions (AI analysis)

   Remaining 5-10% flagged with explanations

7. Manual review

   Bookkeeper reviews only flagged items

Advanced Pattern Matching

Check Number Matching

Bank Pattern: CHECK #(\d+).*\$([\d,]+\.\d{2})
GL Pattern: Check (\d+).*\$([\d,]+\.\d{2})

Match if:
- Check numbers identical (Group 1)
- Amounts identical (Group 2)
- Dates within 5 days (AI validates)

ACH/Wire Transfer Matching

Pattern: (ACH|WIRE|EFT)\s+([A-Z0-9]+)

Extract transaction ID (Group 2)
Match by ID + amount
AI handles variations in description

Discrepancy Analysis

Issue	Regex Detection	AI Resolution
Timing difference	Same amount, off by 1-3 days	Analyze if reasonable delay
Amount off by cents	Pattern: within $0.10	Suggest rounding issue
Missing transaction	No regex match found	Categorize as unrecorded
Duplicate entry	Exact pattern match 2x	Verify if legitimate

Time Savings Analysis

Task	Manual	Regex+AI	Savings
Match 200 transactions	4-6 hours	15-30 min	90% faster
Find discrepancies	1-2 hours	5 min	95% faster
Document variances	30 min	2 min	93% faster

Conclusion

Bank reconciliation automation represents one of the highest-value applications of regex + AI in bookkeeping. By using regex for precise pattern matching and AI for contextual intelligence, bookkeepers can reduce reconciliation time by 90% while improving accuracy and catching issues that might be missed in manual review.