{"workflow":{"id":14276,"name":" AI-powered fuzzy matching, and assigns confidence scores.","views":68,"recentViews":2,"totalViews":68,"createdAt":"2026-03-23T16:56:38.580Z","description":"\n\n## Overview\nThis workflow automates financial reconciliation across multiple data sources such as bank statements, invoices, ERP systems, and CSV uploads.\n\nIt 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.\n\n---\n\n## How It Works\n\n1. **Data Ingestion**\n   Receives financial data via webhook from different sources.\n\n2. **Source Detection & Routing**\n   Identifies the data type and routes it to the correct normalization flow.\n\n3. **Data Normalization**\n   Converts all records into a unified schema with consistent fields like ID, amount, date, and description.\n\n4. **Data Merging**\n   Combines all normalized records into a single dataset for matching.\n\n5. **Deterministic Matching**\n   Matches records using exact field combinations such as ID, amount, and date to generate initial confidence.\n\n6. **Match Quality Check**\n   Filters low-confidence matches for further analysis.\n\n7. **AI Fuzzy Matching**\n   Uses AI to identify near matches based on descriptions, amount tolerance, and date proximity.\n\n8. **Confidence Scoring**\n   Combines deterministic and AI results into a final confidence score with a detailed audit trail.\n\n9. **Decision Routing**\n   - High confidence → auto-reconciled  \n   - Low confidence → flagged for human review  \n\n10. **Reporting**\n   Logs reconciliation results into Google Sheets.\n\n11. **Notifications**\n   Sends a summary report to Slack for visibility.\n\n---\n\n## Setup Instructions\n\n- Configure webhook to receive financial data  \n- Set matching keys and confidence thresholds  \n- Connect OpenAI for fuzzy matching  \n- Connect Google Sheets for reporting  \n- Connect Slack for notifications  \n- Ensure input data follows expected formats  \n- Test with sample financial data  \n- Activate the workflow  \n\n---\n\n## Use Cases\n\n- Bank statement vs invoice reconciliation  \n- ERP vs accounting system matching  \n- Financial audit automation  \n- Detecting missing or duplicate transactions  \n- Reducing manual reconciliation effort  \n\n---\n\n## Requirements\n\n- n8n instance with webhook support  \n- OpenAI API access  \n- Google Sheets account  \n- Slack workspace  \n- Structured financial datasets (CSV/API)  \n\n---\n\n## Notes\n\n- Deterministic matching ensures accuracy for exact matches.\n- AI fuzzy matching improves coverage for ambiguous records.\n- Confidence scoring provides transparency and auditability.\n- Human review ensures control over uncertain reconciliations.","workflow":{"meta":{"instanceId":"48aac30adfc5487a33ef16e0e958096f27cef40df3ed0febcbe1ca199e789786"},"nodes":[{"id":"42217665-1184-40a8-bddf-b40f9e84fce1","name":"Webhook - Receive Financial Data","type":"n8n-nodes-base.webhook","position":[-768,288],"webhookId":"f212e492-45d2-44e3-801a-ed0d7c9c0351","parameters":{"path":"financial-data-reconciliation","options":{},"httpMethod":"POST","responseMode":"lastNode"},"typeVersion":2.1},{"id":"de790021-1521-4926-bd1a-b40ae43e2794","name":"Workflow Configuration","type":"n8n-nodes-base.set","position":[-592,288],"parameters":{"options":{},"assignments":{"assignments":[{"id":"id-1","name":"confidenceThreshold","type":"number","value":0.85},{"id":"id-2","name":"matchingKeys","type":"array","value":"[\"transactionId\", \"invoiceNumber\", \"amount\", \"date\"]"},{"id":"id-3","name":"fuzzyMatchThreshold","type":"number","value":0.7}]},"includeOtherFields":true},"typeVersion":3.4},{"id":"9f455d80-262a-4af3-85ed-143db95c4696","name":"Check Data Source Type","type":"n8n-nodes-base.if","position":[-368,288],"parameters":{"options":{},"conditions":{"options":{"version":3,"leftValue":"","caseSensitive":true,"typeValidation":"strict"},"combinator":"and","conditions":[{"id":"id-1","operator":{"type":"string","operation":"equals"},"leftValue":"={{ $json.sourceType }}","rightValue":"bank_statement"}]}},"typeVersion":2.3},{"id":"3beba561-8803-4b92-b81e-d5aea0832d57","name":"Extract CSV Data","type":"n8n-nodes-base.extractFromFile","position":[16,48],"parameters":{"options":{}},"typeVersion":1.1},{"id":"5fd65063-4ddc-441b-97b3-9f75d88c32a2","name":"Normalize Bank Statement Schema","type":"n8n-nodes-base.set","position":[16,224],"parameters":{"options":{},"assignments":{"assignments":[{"id":"id-1","name":"recordId","type":"string","value":"={{ $json.transaction_id }}"},{"id":"id-2","name":"amount","type":"number","value":"={{ $json.transaction_amount }}"},{"id":"id-3","name":"date","type":"string","value":"={{ $json.transaction_date }}"},{"id":"id-4","name":"description","type":"string","value":"={{ $json.transaction_description }}"},{"id":"id-5","name":"sourceType","type":"string","value":"bank_statement"},{"id":"id-6","name":"normalizedAt","type":"string","value":"={{ $now.toISO() }}"}]},"includeOtherFields":true},"typeVersion":3.4},{"id":"cd62e763-8cd1-46bb-82b6-f18078412dfb","name":"Normalize Invoice Schema","type":"n8n-nodes-base.set","position":[16,400],"parameters":{"options":{},"assignments":{"assignments":[{"id":"id-1","name":"recordId","type":"string","value":"={{ $json.invoice_number }}"},{"id":"id-2","name":"amount","type":"number","value":"={{ $json.invoice_total }}"},{"id":"id-3","name":"date","type":"string","value":"={{ $json.invoice_date }}"},{"id":"id-4","name":"description","type":"string","value":"={{ $json.invoice_description }}"},{"id":"id-5","name":"sourceType","type":"string","value":"invoice"},{"id":"id-6","name":"normalizedAt","type":"string","value":"={{ $now.toISO() }}"}]},"includeOtherFields":true},"typeVersion":3.4},{"id":"0efc2451-3c62-4988-85d6-e06312afa34c","name":"Normalize ERP Schema","type":"n8n-nodes-base.set","position":[16,608],"parameters":{"options":{},"assignments":{"assignments":[{"id":"id-1","name":"recordId","type":"string","value":"={{ $json.erp_transaction_id }}"},{"id":"id-2","name":"amount","type":"number","value":"={{ $json.erp_amount }}"},{"id":"id-3","name":"date","type":"string","value":"={{ $json.erp_date }}"},{"id":"id-4","name":"description","type":"string","value":"={{ $json.erp_description }}"},{"id":"id-5","name":"sourceType","type":"string","value":"erp"},{"id":"id-6","name":"normalizedAt","type":"string","value":"={{ $now.toISO() }}"}]},"includeOtherFields":true},"typeVersion":3.4},{"id":"0c4e99b7-8eda-4216-aba0-c14e076285c5","name":"Normalize CSV Schema","type":"n8n-nodes-base.set","position":[224,48],"parameters":{"options":{},"assignments":{"assignments":[{"id":"id-1","name":"recordId","type":"string","value":"={{ $json.id }}"},{"id":"id-2","name":"amount","type":"number","value":"={{ $json.amount }}"},{"id":"id-3","name":"date","type":"string","value":"={{ $json.date }}"},{"id":"id-4","name":"description","type":"string","value":"={{ $json.description }}"},{"id":"id-5","name":"sourceType","type":"string","value":"csv_upload"},{"id":"id-6","name":"normalizedAt","type":"string","value":"={{ $now.toISO() }}"}]},"includeOtherFields":true},"typeVersion":3.4},{"id":"a8f3c054-a206-4a16-99d0-2957eb87caa6","name":"Merge All Normalized Data","type":"n8n-nodes-base.merge","position":[448,208],"parameters":{"mode":"combine","options":{},"combineBy":"combineAll"},"typeVersion":3.2},{"id":"b54e7970-4c2b-478c-9568-57a10757895d","name":"Deterministic Matching Logic","type":"n8n-nodes-base.code","position":[832,304],"parameters":{"jsCode":"// Deterministic Matching Logic\n// Groups records by exact matches on recordId, amount, and date fields\n// Calculates match confidence based on number of matching fields\n\nconst items = $input.all();\nconst matchedGroups = {};\nconst results = [];\n\n// Helper function to normalize values for comparison\nfunction normalizeValue(value) {\n  if (value === null || value === undefined) return '';\n  return String(value).trim().toLowerCase();\n}\n\n// Helper function to normalize date for comparison\nfunction normalizeDate(date) {\n  if (!date) return '';\n  try {\n 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'high_confidence' : 'medium_confidence'\n    });\n  } else if (group.items.length === 1) {\n    // Single item - no match found\n    results.push({\n      matchKey: key,\n      matchedFields: group.matchedFields,\n      matchCount: 1,\n      confidence: 0,\n      matchType: 'no_match',\n      records: group.items,\n      status: 'unmatched'\n    });\n  }\n}\n\n// Return results\nreturn results.map(result => ({ json: result }));"},"typeVersion":2},{"id":"895fed31-1878-4834-bdff-834c29948de5","name":"Check Match Quality","type":"n8n-nodes-base.if","position":[1008,304],"parameters":{"options":{},"conditions":{"options":{"leftValue":"","caseSensitive":false,"typeValidation":"loose"},"combinator":"and","conditions":[{"id":"id-1","operator":{"type":"number","operation":"lt"},"leftValue":"={{ $('Deterministic Matching Logic').item.json.matchConfidence }}","rightValue":"={{ $('Workflow Configuration').first().json.fuzzyMatchThreshold }}"}]}},"typeVersion":2.3},{"id":"73cfb80a-0342-4542-a099-9ca83d8b045e","name":"AI Fuzzy Matching Agent","type":"@n8n/n8n-nodes-langchain.agent","position":[1248,528],"parameters":{"text":"=Records to match: {{ $json.unmatchedRecords }}","options":{"systemMessage":"You are a financial data reconciliation specialist. Your task is to analyze unmatched financial records and identify potential matches using fuzzy matching techniques.\n\nFor each unmatched record, you should:\n1. Compare transaction descriptions using semantic similarity\n2. Check for near-matches in amounts (within 1% tolerance)\n3. Look for date matches within a 3-day window\n4. Consider vendor name variations and abbreviations\n5. Return matched pairs with confidence scores\n\nReturn your analysis in the structured format defined by the output parser."},"promptType":"define","hasOutputParser":true},"typeVersion":3},{"id":"7abbe9b8-2a0b-48d9-81c0-39b0a80df771","name":"OpenAI Chat Model","type":"@n8n/n8n-nodes-langchain.lmChatOpenAi","position":[1248,864],"parameters":{"model":{"__rl":true,"mode":"list","value":"gpt-4.1-mini"},"options":{},"builtInTools":{}},"typeVersion":1.3},{"id":"2a37407e-1fd9-46d5-a942-de40e246f47d","name":"Structured Output Parser","type":"@n8n/n8n-nodes-langchain.outputParserStructured","position":[1408,848],"parameters":{"jsonSchemaExample":"{\n\t\"matchedPairs\": [\n\t\t{\n\t\t\t\"sourceRecord\": {},\n\t\t\t\"targetRecord\": {},\n\t\t\t\"confidenceScore\": 0.95,\n\t\t\t\"matchReason\": \"Exact match on transaction ID and amount\"\n\t\t}\n\t],\n\t\"unmatchedRecords\": []\n}"},"typeVersion":1.3},{"id":"295397ed-0102-4df9-a2cf-446a85439e49","name":"Merge Matched Results","type":"n8n-nodes-base.merge","position":[1680,304],"parameters":{"mode":"combineAll"},"typeVersion":3.2},{"id":"54f8e770-f6f8-4b49-bb2c-78962732ed6f","name":"Calculate Confidence Scores","type":"n8n-nodes-base.code","position":[1872,304],"parameters":{"mode":"runOnceForEachItem","jsCode":"// Calculate final confidence scores by combining deterministic and AI fuzzy match scores\n// Normalize to 0-1 range and add audit trail metadata\n\nconst item = $input.item.json;\n\n// Extract scores from different matching stages\nconst deterministicScore = item.deterministicMatchScore || 0;\nconst aiScore = item.aiMatchScore || 0;\nconst hasAiMatch = item.aiMatchScore !== undefined;\n\n// Weight configuration\nconst DETERMINISTIC_WEIGHT = 0.6;\nconst AI_WEIGHT = 0.4;\n\n// Calculate combined confidence score\nlet finalConfidence;\nif (hasAiMatch) {\n  // Combine both scores with weights\n  finalConfidence = (deterministicScore * DETERMINISTIC_WEIGHT) + (aiScore * AI_WEIGHT);\n} else {\n  // Only deterministic match available\n  finalConfidence = deterministicScore;\n}\n\n// Normalize to 0-1 range\nfinalConfidence = Math.max(0, Math.min(1, finalConfidence));\n\n// Determine confidence level\nlet confidenceLevel;\nif (finalConfidence >= 0.9) {\n  confidenceLevel = 'HIGH';\n} else if (finalConfidence >= 0.7) {\n  confidenceLevel = 'MEDIUM';\n} else if (finalConfidence >= 0.5) {\n  confidenceLevel = 'LOW';\n} else {\n  confidenceLevel = 'VERY_LOW';\n}\n\n// Build audit trail\nconst auditTrail = {\n  timestamp: new Date().toISOString(),\n  deterministicScore: deterministicScore,\n  aiScore: hasAiMatch ? aiScore : null,\n  finalConfidence: finalConfidence,\n  confidenceLevel: confidenceLevel,\n  matchingMethod: hasAiMatch ? 'HYBRID' : 'DETERMINISTIC',\n  weights: {\n    deterministic: DETERMINISTIC_WEIGHT,\n    ai: AI_WEIGHT\n  }\n};\n\n// Return enriched item with confidence scores and audit trail\nreturn {\n  json: {\n    ...item,\n    finalConfidence: finalConfidence,\n    confidenceLevel: confidenceLevel,\n    confidencePercentage: Math.round(finalConfidence * 100),\n    auditTrail: auditTrail\n  }\n};"},"typeVersion":2},{"id":"e96842c2-5bc0-44e0-b3e9-bfcbb62a80e6","name":"Route by Confidence Threshold","type":"n8n-nodes-base.if","position":[2080,320],"parameters":{"options":{},"conditions":{"options":{"leftValue":"","caseSensitive":false,"typeValidation":"loose"},"combinator":"and","conditions":[{"id":"id-1","operator":{"type":"number","operation":"gte"},"leftValue":"={{ $json.finalConfidence }}","rightValue":"={{ $('Workflow Configuration').first().json.confidenceThreshold }}"}]}},"typeVersion":2.3},{"id":"b10d37cf-b377-4397-8b67-f358bfc5da07","name":"Flag for Human Review","type":"n8n-nodes-base.set","position":[2320,400],"parameters":{"options":{},"assignments":{"assignments":[{"id":"id-1","name":"reviewStatus","type":"string","value":"pending_human_review"},{"id":"id-2","name":"reviewReason","type":"string","value":"Low confidence match - requires manual verification"},{"id":"id-3","name":"flaggedAt","type":"string","value":"={{ $now.toISO() }}"}]},"includeOtherFields":true},"typeVersion":3.4},{"id":"52559270-484a-49d0-847e-f004518abb02","name":"Mark as Auto-Reconciled","type":"n8n-nodes-base.set","position":[2320,256],"parameters":{"options":{},"assignments":{"assignments":[{"id":"id-1","name":"reviewStatus","type":"string","value":"auto_reconciled"},{"id":"id-2","name":"reconciledAt","type":"string","value":"={{ $now.toISO() }}"}]},"includeOtherFields":true},"typeVersion":3.4},{"id":"fe27e284-4bc7-446b-85fc-1cd03f8b8f81","name":"Merge All Results","type":"n8n-nodes-base.merge","position":[2640,352],"parameters":{},"typeVersion":3.2},{"id":"4cdd473a-2795-43ed-a464-a874d458e3ff","name":"Log to Reconciliation Report","type":"n8n-nodes-base.googleSheets","position":[2864,352],"parameters":{"columns":{"value":null,"mappingMode":"autoMapInputData"},"options":{},"operation":"appendOrUpdate","sheetName":{"__rl":true,"mode":"name","value":"Reconciliation Report"},"documentId":{"__rl":true,"mode":"id","value":"<__PLACEHOLDER_VALUE__Google Sheets Document ID__>"}},"typeVersion":4.7},{"id":"2682e628-ed05-4fd2-8feb-43d9caff7470","name":"Notify Finance Team","type":"n8n-nodes-base.slack","position":[3120,352],"webhookId":"cdd2cef7-c169-49a6-9da8-a331c1134920","parameters":{"text":"=Reconciliation Complete!\n\nTotal Records Processed: {{ $json.totalRecords }}\nAuto-Reconciled: {{ $json.autoReconciledCount }}\nPending Review: {{ $json.pendingReviewCount }}\n\nView full report: {{ $json.reportUrl }}","select":"channel","channelId":{"__rl":true,"mode":"id","value":"<__PLACEHOLDER_VALUE__Slack Channel ID or Name__>"},"otherOptions":{}},"typeVersion":2.4},{"id":"3d42b58b-ab5e-4969-bbd9-96444b55ee6c","name":"Merge All Normalized Data1","type":"n8n-nodes-base.merge","position":[448,432],"parameters":{"mode":"combine","options":{},"combineBy":"combineAll"},"typeVersion":3.2},{"id":"e879468a-0a17-4a40-ada7-79e8023272e2","name":"Merge All Normalized Data2","type":"n8n-nodes-base.merge","position":[640,304],"parameters":{"mode":"combine","options":{},"combineBy":"combineAll"},"typeVersion":3.2},{"id":"c8f83a37-b017-4749-99da-e51966fabb13","name":"Sticky Note","type":"n8n-nodes-base.stickyNote","position":[-1664,64],"parameters":{"width":560,"height":528,"content":"\n## How it works\nThis workflow automates reconciliation across bank statements, invoices, ERP systems, and CSV uploads. It standardizes all data into a unified format and performs deterministic matching using fields like ID, amount, and date.\n\nFor unmatched records, AI fuzzy matching identifies potential matches using descriptions, amount tolerance, and date proximity. A final confidence score is calculated, auto-reconciling high-confidence matches while flagging others for review.\n\n## Setup\n\n- Configure webhook to receive financial data  \n- Set matching keys and confidence thresholds  \n- Connect OpenAI for fuzzy matching  \n- Connect Google Sheets for reporting  \n- Connect Slack for notifications  \n- Ensure input data follows expected formats  \n- Test with sample data before activating  \n"},"typeVersion":1},{"id":"d97902db-baa8-41af-8e92-60ed7b090c42","name":"Sticky Note1","type":"n8n-nodes-base.stickyNote","position":[-864,144],"parameters":{"color":7,"width":400,"height":352,"content":"## Data Input\nReceives financial data via webhook from multiple sources and Defines matching keys, thresholds, and reconciliation rules."},"typeVersion":1},{"id":"1f5d7acb-20d4-4968-ae11-15a950fb1d82","name":"Sticky Note2","type":"n8n-nodes-base.stickyNote","position":[-432,144],"parameters":{"color":7,"width":256,"height":352,"content":"## Source Routing\nDetects data type and routes to correct normalization flow.)"},"typeVersion":1},{"id":"d5f1e7f2-2466-407a-bf7e-863fefa466f5","name":"Sticky Note3","type":"n8n-nodes-base.stickyNote","position":[-96,-80],"parameters":{"color":7,"width":272,"height":848,"content":"## Data Normalization\nConverts bank, invoice, ERP, and CSV data into a unified schema."},"typeVersion":1},{"id":"57d64771-fda3-4bd2-9b0a-d5e60537f5ae","name":"Sticky Note4","type":"n8n-nodes-base.stickyNote","position":[384,80],"parameters":{"color":7,"width":384,"height":528,"content":"## Data Merge\nCombines all normalized records for comparison."},"typeVersion":1},{"id":"f93866d8-35f9-4065-8a18-d16ce408a60a","name":"Sticky Note5","type":"n8n-nodes-base.stickyNote","position":[800,128],"parameters":{"color":7,"width":352,"height":384,"content":"## Deterministic Matching\nMatches records using exact fields like ID, amount, and date."},"typeVersion":1},{"id":"1d8eb26d-23ee-4ca0-9620-3bd8775e7559","name":"Sticky Note6","type":"n8n-nodes-base.stickyNote","position":[1200,416],"parameters":{"color":7,"width":336,"height":352,"content":"## AI Fuzzy Matching\nUses AI to find near matches based on text, amount, and date."},"typeVersion":1},{"id":"10ca8421-c5f2-4033-a2e5-bf8f845322e1","name":"Sticky Note7","type":"n8n-nodes-base.stickyNote","position":[1584,192],"parameters":{"color":7,"width":432,"height":272,"content":"## Confidence Scoring\nCombines matching results into final confidence score with audit trail."},"typeVersion":1},{"id":"89ed1034-6845-4de2-8a5c-5a235c58bf7e","name":"Sticky Note8","type":"n8n-nodes-base.stickyNote","position":[2048,176],"parameters":{"color":7,"width":464,"height":416,"content":"## Decision Routing\nRoutes high-confidence matches or flags low-confidence for review."},"typeVersion":1},{"id":"ef0d6bea-dbd9-42cb-b9e6-6fdac191bbd4","name":"Sticky Note9","type":"n8n-nodes-base.stickyNote","position":[2832,176],"parameters":{"color":7,"width":480,"height":416,"content":"## Reporting\nLogs reconciliation results into Google Sheets and Sends summary of reconciliation results via Slack."},"typeVersion":1}],"pinData":{},"connections":{"Extract CSV Data":{"main":[[{"node":"Normalize CSV Schema","type":"main","index":0}]]},"Merge All Results":{"main":[[{"node":"Log to Reconciliation Report","type":"main","index":0}]]},"OpenAI Chat Model":{"ai_languageModel":[[{"node":"AI Fuzzy Matching Agent","type":"ai_languageModel","index":0}]]},"Check Match Quality":{"main":[[{"node":"Merge Matched Results","type":"main","index":0}],[{"node":"AI Fuzzy Matching Agent","type":"main","index":0}]]},"Normalize CSV Schema":{"main":[[{"node":"Merge All Normalized Data","type":"main","index":0}]]},"Normalize ERP Schema":{"main":[[{"node":"Merge All Normalized Data1","type":"main","index":1}]]},"Flag for Human Review":{"main":[[{"node":"Merge All 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