AI-powered fuzzy matching, and assigns confidence scores.

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.