🔬 AI-powered fuzzy matching, and assigns confidence scores.
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Description
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
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Data Ingestion Receives financial data via webhook from different sources.
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Source Detection & Routing Identifies the data type and routes it to the correct normalization flow.
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Data Normalization Converts all records into a unified schema with consistent fields like ID, amount, date, and description.
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Data Merging Combines all normalized records into a single dataset for matching.
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Deterministic Matching Matches records using exact field combinations such as ID, amount, and date to generate initial confidence.
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Match Quality Check Filters low-confidence matches for further analysis.
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AI Fuzzy Matching Uses AI to identify near matches based on descriptions, amount tolerance, and date proximity.
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Confidence Scoring Combines deterministic and AI results into a final confidence score with a detailed audit trail.
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Decision Routing
- High confidence → auto-reconciled
- Low confidence → flagged for human review
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Reporting Logs reconciliation results into Google Sheets.
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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.
🔗 Nodes Used
Google Sheets, Slack, Webhook, AI Agent, OpenAI Chat Model, Structured Output Parser
📥 Import
Download workflow.json and import into n8n:
Workflow menu → Import from File