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Created by: ResilNext || rnair1996
ResilNext

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Last update 9 hours ago

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Overview

This workflow automates financial reconciliation across multiple data sources such as bank statements, invoices, ERP systems, and CSV uploads.

It standardizes all incoming data, performs rule-based matching, enhances results with AI-powered fuzzy matching, and assigns confidence scores. High-confidence matches are auto-reconciled, while uncertain ones are flagged for human review.

How It Works

  1. Data Ingestion
    Receives financial data via webhook from different sources.

  2. Source Detection & Routing
    Identifies the data type and routes it to the correct normalization flow.

  3. Data Normalization
    Converts all records into a unified schema with consistent fields like ID, amount, date, and description.

  4. Data Merging
    Combines all normalized records into a single dataset for matching.

  5. Deterministic Matching
    Matches records using exact field combinations such as ID, amount, and date to generate initial confidence.

  6. Match Quality Check
    Filters low-confidence matches for further analysis.

  7. AI Fuzzy Matching
    Uses AI to identify near matches based on descriptions, amount tolerance, and date proximity.

  8. Confidence Scoring
    Combines deterministic and AI results into a final confidence score with a detailed audit trail.

  9. Decision Routing

    • High confidence → auto-reconciled
    • Low confidence → flagged for human review

  10. Reporting
    Logs reconciliation results into Google Sheets.

  11. Notifications
    Sends a summary report to Slack for visibility.

Setup Instructions

  • Configure webhook to receive financial data
  • Set matching keys and confidence thresholds
  • Connect OpenAI for fuzzy matching
  • Connect Google Sheets for reporting
  • Connect Slack for notifications
  • Ensure input data follows expected formats
  • Test with sample financial data
  • Activate the workflow

Use Cases

  • Bank statement vs invoice reconciliation
  • ERP vs accounting system matching
  • Financial audit automation
  • Detecting missing or duplicate transactions
  • Reducing manual reconciliation effort

Requirements

  • n8n instance with webhook support
  • OpenAI API access
  • Google Sheets account
  • Slack workspace
  • Structured financial datasets (CSV/API)

Notes

  • Deterministic matching ensures accuracy for exact matches.
  • AI fuzzy matching improves coverage for ambiguous records.
  • Confidence scoring provides transparency and auditability.
  • Human review ensures control over uncertain reconciliations.