alexi5000/skills/bank-reconciliation

OpenClaw skill context: This skill supports ClawKeeper v1.5 as an OpenClaw-native SMB finance-agent platform. Implementations should preserve tenant isolation, deterministic policy enforcement, and auditable financial operations.

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name: bank-reconciliation description: "Match bank transactions to book transactions, detect discrepancies, and resolve mismatches. Use when reconciling bank accounts, investigating unmatched transactions, or generating reconciliation reports. Handles multiple accounts and currencies."

Bank Reconciliation Skill

Purpose

Automates the bank reconciliation process by matching book transactions to bank statement transactions, detecting discrepancies, and facilitating resolution.

Triggers

• End of month reconciliation scheduled
• User initiates manual reconciliation
• Discrepancy detected in account balance
• Bank feed imported via Plaid

Capabilities

1. Transaction Import - Import bank transactions via Plaid or CSV
2. Transaction Matching - Match book to bank transactions
3. Discrepancy Detection - Identify unmatched transactions
4. Fuzzy Matching - Match similar amounts and dates
5. Outstanding Items - Track checks and deposits in transit
6. Balance Verification - Verify book balance = cleared bank balance + outstanding items

Instructions

Step 1: Import Bank Transactions

Options:

1. Plaid Integration - Fetch via data-sync skill
2. CSV Upload - Parse CSV file with columns: date, description, amount
3. Manual Entry - User inputs transactions

Step 2: Load Book Transactions

Query database for unreconciled transactions in date range:

SELECT * FROM transactions 
WHERE account_id = $1 
AND date BETWEEN $2 AND $3 
AND reconciled = FALSE

Step 3: Exact Matching

Match transactions where:

• Date matches exactly
• Amount matches exactly (cents)
• Payee name matches (after normalization)

Mark as reconciled = TRUE when matched.

Step 4: Fuzzy Matching

For unmatched transactions, try fuzzy match:

• Date proximity - Within ±3 days
• Amount match - Exact amount
• Payee similarity - Levenshtein distance < 3 or substring match

Present fuzzy matches for manual review.

Step 5: Discrepancy Detection

Identify discrepancies:

• Missing from Bank - In books but not in bank statement (check in transit?)
• Missing from Books - In bank but not in books (unrecorded transaction?)
• Amount Mismatch - Same transaction, different amounts (data entry error?)
• Date Mismatch - Same transaction, different dates (timing difference?)

Step 6: Outstanding Items

Track outstanding items:

• Checks Written - In books, not yet cleared bank
• Deposits Made - In books, not yet posted by bank

Calculate outstanding balance:

Book Balance - Outstanding Checks + Outstanding Deposits = Bank Balance

Step 7: Generate Report

Create reconciliation report:

{
  "account_id": "uuid",
  "period_start": "2026-01-01",
  "period_end": "2026-01-31",
  "book_balance": 5000000,  // cents
  "bank_balance": 4950000,  // cents
  "matched_count": 45,
  "unmatched_book_count": 2,
  "unmatched_bank_count": 1,
  "outstanding_checks": [
    {"id": "...", "amount": -25000, "payee": "..."}
  ],
  "outstanding_deposits": [
    {"id": "...", "amount": 75000}
  ],
  "discrepancies": [
    {
      "type": "missing_from_bank",
      "transaction_id": "...",
      "description": "...",
      "amount": -5000
    }
  ],
  "status": "balanced",  // or "discrepancy"
  "requires_investigation": false
}

Decision Logic

Auto-Reconcile

• Exact match (date, amount, payee)
• Previously reconciled (if re-running)

Manual Review Required

• Fuzzy match (present options to user)
• Significant discrepancy (> $100)
• Unusual pattern (e.g., duplicate amounts)

Escalate

• Unresolved after 3 reconciliation attempts
• Balance off by > $1000
• Fraud indicators detected

Integration Points

• data-sync (via Integration Lead) - For Plaid import
• transaction-matcher (Reconciliation worker) - For matching logic
• discrepancy-investigator (Reconciliation worker) - For investigation
• audit-trail - Log all reconciliation actions

Models

• Matching: Deterministic algorithm (no LLM)
• Payee Normalization: Claude Sonnet 4 or Gemini Flash
• Discrepancy Investigation: Claude Sonnet 4

Security

• Never modify historical transactions automatically
• Require manual approval for adjustments
• Log all reconciliation actions to audit_log
• Preserve original bank data

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Invoke this skill at month-end or when investigating account discrepancies.