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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

Data Ingestion Receives financial data via webhook from different sources.

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

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

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

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

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

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

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

Decision Routing High confidence → auto-reconciled
Low confidence → flagged for human review

Reporting Logs reconciliation results into Google Sheets.

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.