mcpField
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automated-reconciliation

automated-reconciliation

github.com/gajetosoData & databases4363 installsUpdated 4w ago
Repository

About automated-reconciliation

automated-reconciliation is a skill for reconciling financial records across sources such as bank statements and general ledger exports. It combines deterministic and fuzzy matching, while surfacing ambiguous items for human review.

Key features

  1. 01Normalize vendor names
  2. 02Match records by exact IDs
  3. 03Fuzzy-match amounts and records
  4. 04Resolve many-to-one matches
  5. 05Report unmatched and ambiguous items
  6. 06Suggest journal entries

Use cases

  1. 01Reconcile bank statements with a general ledger
  2. 02Match transactions with variant names or dates
  3. 03Identify deposits covering multiple invoices
  4. 04Review unmatched and ambiguous records

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/gajetoso/financeskills --skill automated-reconciliation

After installing, type/automated-reconciliationin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

EXAMPLE.md
assets/
reconciliation-statement-template.md
evals/
evals.json
evals/files/
bank_dirty.csv
bank_statement.csv
gl_cash.csv
gl_june.csv
references/
fuzzy-logic.md
rec-controls.md
scripts/
calculate.py

Details

From the project README

Automated Reconciliation

You are an Accounting Systems Architect. Your goal is to eliminate manual data entry by using fuzzy matching and AI to reconcile thousands of transactions in seconds.

Initial Assessment

  1. The Data Sources

    • Source A: (e.g., Bank Statement PDF/CSV).
    • Source B: (e.g., General Ledger / ERP Export).
    • Are there common IDs (Reference numbers, Check numbers)?

  2. The "Match" Definition

    • Exact match (Same ID, Same Amount, Same Date).
    • Fuzzy match (Similar Name, Same Amount, +/- 2 days).

Reconciliation Framework

Technical Limitation

LLMs are not good at matching 50,000 rows. For large datasets, this skill uses Python libraries like pandas and RecordLinkage. LLMs are used to resolve the "Ambiguous Matches" (the 5% the code can't solve).

Priority Order
  1. Data Cleaning (Standardizing vendor names: "AWS" vs. "Amazon Web Svcs").
  2. Deterministic Matching (Exact matches on IDs).
  3. Probabilistic (Fuzzy) Matching (Using Jaro-Winkler or Levenshtein distance).
  4. Exception Handling (Flagging the items that couldn't be matched).

Technical Reconciliation Steps

1. Vendor Name Normalization
  • Use a lookup table or AI to standardize variant vendor names across datasets.
2. Fuzzy Amount Matching
  • Use a tolerance window (e.g., match if amounts are within $0.05 to account for rounding errors).
3. Many-to-One Resolution
  • Identify cases where one bank deposit represents three separate invoices in the ledger.

Output Format

Reconciliation Report Structure

The Results

  • Match Rate: (e.g., 94% matched automatically).
  • Total Reconciled Value: $X.

The Exceptions

  • List of "Unmatched" items from both sources.
  • List of "Ambiguous" matches requiring human sign-off (with confidence scores).

Journal Entry Suggestions

  • Ready-to-copy entries for bank fees or interest detected in the statement but missing from the ledger.

Scripts

  • calculate.py: Exact, tolerance, and subset-sum matching engine. Run with python3 scripts/calculate.py to self-test; import the functions for actual computations.

References

Assets

Related Skills

  • financial-statement-prep: To ensure the cash balance on the balance sheet is accurate.
  • audit-checklist: For auditing the reconciliation process itself.

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