キャッシュの照合
中級
これはContent Creation, Multimodal AI分野の自動化ワークフローで、15個のノードを含みます。主にCode, MistralAi, ManualTrigger, MicrosoftExcel, Agentなどのノードを使用。 Mistral AI および OpenAI GPT-4 を用いた請求書と銀行通帳照合の自動化
前提条件
- •OpenAI API Key
ワークフロープレビュー
ノード接続関係を可視化、ズームとパンをサポート
ワークフローをエクスポート
以下のJSON設定をn8nにインポートして、このワークフローを使用できます
{
"id": "n4bNr0cnmlkuN8fy",
"meta": {
"instanceId": "db1715da5f21adba44ce4ea3b08abb06cd1771e876f5ad2751fcafd78c5eb9dc",
"templateCredsSetupCompleted": true
},
"name": "CashReconciliation",
"tags": [
{
"id": "7Hqs1zOnO1KyMmlS",
"name": "Cashreconciliation",
"createdAt": "2025-09-25T22:03:57.474Z",
"updatedAt": "2025-09-25T22:03:57.474Z"
},
{
"id": "HdENOIIKDc5O1stL",
"name": "Accountant",
"createdAt": "2025-09-25T22:04:06.515Z",
"updatedAt": "2025-09-25T22:04:06.515Z"
},
{
"id": "kAdvJMsTQvyVnrF9",
"name": "AccountReceivable",
"createdAt": "2025-09-25T22:04:25.528Z",
"updatedAt": "2025-09-25T22:04:25.528Z"
},
{
"id": "lsoR6uHgfiOrR6C6",
"name": "OrdertoCash",
"createdAt": "2025-09-25T22:04:29.775Z",
"updatedAt": "2025-09-25T22:04:29.775Z"
},
{
"id": "uKun50piys98JE3C",
"name": "Invoices",
"createdAt": "2025-09-18T00:47:51.695Z",
"updatedAt": "2025-09-18T00:47:51.695Z"
}
],
"nodes": [
{
"id": "451c356d-2215-4c59-8f92-40678b080c2b",
"name": "ワークフロー実行時",
"type": "n8n-nodes-base.manualTrigger",
"position": [
224,
0
],
"parameters": {},
"typeVersion": 1
},
{
"id": "8e42332e-f1cb-41f8-a0e1-f37b6ab3e75a",
"name": "テキスト抽出",
"type": "n8n-nodes-base.mistralAi",
"position": [
1344,
0
],
"parameters": {
"options": {
"batch": false
},
"binaryProperty": "Data"
},
"credentials": {
"mistralCloudApi": {
"id": "<Mistral OCR API KEY>",
"name": "Mistral Cloud account"
}
},
"typeVersion": 1
},
{
"id": "a8014539-db77-4ade-97c5-d42077119291",
"name": "銀行取引明細書の取得",
"type": "n8n-nodes-base.microsoftOneDrive",
"position": [
896,
0
],
"parameters": {
"fileId": "01WVQSKIIAS4II25G37JGK6QHSYCDROS76",
"operation": "get"
},
"credentials": {
"microsoftOneDriveOAuth2Api": {
"id": "<Microsoft One Drive API KEY>",
"name": "Microsoft Drive account"
}
},
"typeVersion": 1
},
{
"id": "822968f4-1066-417d-9521-d1064f511bb7",
"name": "JavaScriptコード",
"type": "n8n-nodes-base.code",
"position": [
672,
0
],
"parameters": {
"jsCode": "// n8n Code node\n// Input: 1 item that contains `json.data` array\n// Output: one item with a single JSON array erpLedger\n\nconst data = items[0].json.data; // all rows live here\n\nconst ledger = data\n .map(d => {\n const row = d.values?.[0]; // [\"Ansys\", 1, \"08-15-2025\", 5096.96]\n if (!row || row.length < 4) return null;\n\n return {\n CustomerName: row[0], // first column\n invoice_number: row[1], // second column\n invoice_due_date: row[2], // third column\n amount: Number(row[3]), // fourth column\n id: String(row[1]) // use invoice number as ID\n };\n })\n .filter(r => r !== null);\n\nreturn [\n {\n json: {\n erpLedger: ledger\n }\n }\n];\n"
},
"typeVersion": 2
},
{
"id": "3a9cc7b1-aeeb-4f5f-b61f-db5fe3dfc402",
"name": "OpenAI Chat Model1",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
1568,
224
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4.1-mini"
},
"options": {
"temperature": 0
}
},
"credentials": {
"openAiApi": {
"id": "<OPENAI API KEY>",
"name": "OpenAi account 2"
}
},
"typeVersion": 1.2
},
{
"id": "99f88b81-4e58-4a5d-a47a-67f6ba0771a4",
"name": "取引・一致・要約の取得",
"type": "n8n-nodes-base.code",
"position": [
1920,
0
],
"parameters": {
"jsCode": "// n8n Code node\n// Input: one item with .json.output (string containing transactions, matches, summary)\n// Output: multiple items (one per row in the reconciliation table)\n\nconst raw = $input.first().json.output;\n\n// ---------- Step 1: Parse safely ----------\nlet transactions = [];\nlet matches = [];\nlet summary = {};\n\ntry {\n // Normalize separators: replace triple dashes with blank lines\n const normalized = raw.replace(/---/g, \"\\n\\n\");\n\n // Split into JSON blocks\n const blocks = normalized\n .split(/\\n\\s*\\n/)\n .map(b => b.trim())\n .filter(Boolean);\n\n if (blocks[0]) {\n transactions = JSON.parse(blocks[0]);\n }\n if (blocks[1]) {\n matches = JSON.parse(blocks[1]);\n }\n if (blocks[2]) {\n summary = JSON.parse(blocks[2]);\n }\n} catch (e) {\n return [{\n json: { error: \"Parse failed\", message: e.message, rawStart: raw.substring(0, 200) }\n }];\n}\n\n// ---------- Step 2: Index matches by transaction_id ----------\nconst matchMap = {};\nfor (const m of matches) {\n matchMap[m.transaction_id] = {\n ...m,\n // Normalize classification fields\n unmatched_classification: m.unmatched_classification || m.classification || null\n };\n}\n\n// ---------- Step 3: Build reconciliation rows ----------\nconst rows = [];\n\nfor (const txn of transactions) {\n const m = matchMap[txn.transaction_id];\n\n if (m && Array.isArray(m.matches) && m.matches.length > 0) {\n // Matched transaction (can have multiple invoices)\n for (const match of m.matches) {\n rows.push({\n \"Bank Transaction Date\": new Date(txn.date).toLocaleDateString(\"en-US\"),\n \"Bank Transaction Description\": txn.description,\n \"Bank Amount\": txn.amount,\n \"ERP Invoice Number(s)\": match.invoice_number || null,\n \"ERP Customer Name(s)\": \"N/A\", // not provided in your JSON\n \"ERP Amount(s)\": txn.amount,\n \"Match Status\": \"Matched\",\n \"Confidence Score\": match.confidence || null,\n \"Reason\": match.reason || \"\"\n });\n }\n } else {\n // Unmatched transaction\n rows.push({\n \"Bank Transaction Date\": new Date(txn.date).toLocaleDateString(\"en-US\"),\n \"Bank Transaction Description\": txn.description,\n \"Bank Amount\": txn.amount,\n \"ERP Invoice Number(s)\": null,\n \"ERP Customer Name(s)\": \"N/A\",\n \"ERP Amount(s)\": null,\n \"Match Status\": m?.unmatched_classification || \"Unapplied\",\n \"Confidence Score\": null,\n \"Reason\": m?.reason || \"No match\"\n });\n }\n}\n\n// ---------- Step 4: Return as n8n items ----------\nreturn rows.map(r => ({ json: r }));\n"
},
"typeVersion": 2,
"alwaysOutputData": true
},
{
"id": "b9395a80-9e22-4e11-8e8e-85f558cc618f",
"name": "非構造化ファイルからのデータ抽出",
"type": "n8n-nodes-base.microsoftOneDrive",
"position": [
1120,
0
],
"parameters": {
"fileId": "={{ $json.id }}",
"operation": "download",
"binaryPropertyName": "=Data"
},
"credentials": {
"microsoftOneDriveOAuth2Api": {
"id": "<Microsoft One Drive API KEY>",
"name": "Microsoft Drive account"
}
},
"typeVersion": 1
},
{
"id": "464d6980-ee91-4509-b433-012adb2bcf88",
"name": "請求書と銀行取引明細書データの処理",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
1568,
0
],
"parameters": {
"text": "=You are a cash reconciliation specialist.\n\nINPUT DATA:\n- Bank transactions (raw text): {{ $json.extractedText }}\n- ERP ledger entries (JSON): {{ JSON.stringify($('Code in JavaScript').item.json.erpLedger) }}\n\nTASKS\n1) Parse bank text into JSON rows with fields:\n [{\"date\":\"YYYY-MM-DD\",\"description\":\"string\",\"amount\":number,\"currency\":\"string\",\"transaction_id\":\"string\"}]\n2) Match each bank transaction to one or more ERP entries (keys: exact amount, date ±2 days, reference similarity).\n3) Unmatched items: classify as \"unapplied\", \"suspense\", or \"needs_review\" with reasons.\n4) For partial/one-to-many matches, propose splits with allocation amounts.\n5) Provide a summary: total_txns, total_matched, total_unmatched, reconciliation_rate_pct.\n6) Add a confidence score (0–1) and a short reason for each match/split.\n\nCONSTRAINTS\n- Return JSON ONLY. No prose, no markdown.\n- Limit candidates to top 3 per transaction by confidence.\n- If best confidence < 0.6, treat as unmatched.\n- Use transaction_id and invoice numbers from the inputs.\n\nI want the output in Tabular format\n",
"options": {},
"promptType": "define",
"hasOutputParser": true
},
"typeVersion": 2.2
},
{
"id": "84487907-0767-4877-89cf-d8ce56a9ac39",
"name": "付箋",
"type": "n8n-nodes-base.stickyNote",
"position": [
-560,
-432
],
"parameters": {
"color": 4,
"width": 2080,
"height": 272,
"content": "## **Problem Statement**\n### Cash reconciliation is one of the most time-consuming and error-prone processes for Accounts Receivable teams. Every day, specialists need to take the bank statement, scan through hundreds of line items, and manually check which transactions correspond to outstanding invoices in the ERP system. This slows down the month-end close, creates a backlog of unapplied cash, and impacts visibility into actual cash flow.\n\n## **The challenge is twofold**\n\n### Volume & Complexity – Bank statements contain dozens of deposits, withdrawals, fees, and transfers. Invoices may partially match or differ slightly in timing/amounts, making manual matching tedious.\n### Accuracy & Speed – Missing a match means open invoices stay unresolved, while mis-matches lead to reconciliation errors and corrections later in accounting."
},
"typeVersion": 1
},
{
"id": "f02dc91c-34e1-48b5-8aca-51ad5c3001db",
"name": "付箋1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-560,
-64
],
"parameters": {
"color": 6,
"width": 736,
"height": 288,
"content": "## **Value**:\n\n### Time saved: Removes repetitive manual matching.\n### Cash flow visibility: Gives near real-time reconciliation metrics.\n### Error reduction: Uses AI confidence scoring and reasons for unmatched items.\n### Scalability: Can handle daily statement volumes without extra staff."
},
"typeVersion": 1
},
{
"id": "5e1cd291-8e85-4869-ad70-f8a3958ac55b",
"name": "付箋2",
"type": "n8n-nodes-base.stickyNote",
"position": [
208,
176
],
"parameters": {
"color": 4,
"width": 1312,
"height": 176,
"content": "## ***Input***:\n\n### Open invoices are loaded from Excel.\n### Daily bank statement is fetched from OneDrive.\n### OCR extracts transaction data from the statement."
},
"typeVersion": 1
},
{
"id": "a45de2dd-7ee6-4fa9-a167-d22ceab156e2",
"name": "付箋3",
"type": "n8n-nodes-base.stickyNote",
"position": [
1712,
240
],
"parameters": {
"color": 4,
"width": 784,
"height": 240,
"content": "## ***AI Processing***:\n\n### Both invoice data and bank transactions are passed into an OpenAI Chat model.\n### The model evaluates and returns:\n Transaction → Invoice matches\n Confidence scores\n Unmatched transactions with reasons\n Summary metrics (total matched, unmatched, reconciliation %)."
},
"typeVersion": 1
},
{
"id": "123cec6b-f238-4a07-a75f-2646c3ce0106",
"name": "付箋4",
"type": "n8n-nodes-base.stickyNote",
"position": [
208,
368
],
"parameters": {
"color": 4,
"width": 1328,
"height": 496,
"content": "## ***Post-Processing***:\n\n### Custom code nodes parse the AI output.\n### Results are converted into a structured table with columns like:\n\nBank Transaction Date\nDescription\nAmount\nERP Invoice Number(s)\nERP Customer Name(s)\nERP Amount(s)\nMatch Status\nConfidence Score\nReason\n\n## ***Output***:\n\n### The AR specialist sees a ready-made reconciliation table showing exactly which invoices can be closed in the ERP and which need further review. This reduces manual effort, improves reconciliation accuracy, and accelerates cash application."
},
"typeVersion": 1
},
{
"id": "38449472-1724-44cc-aa6b-af80c8eaeb6b",
"name": "請求書データの取得",
"type": "n8n-nodes-base.microsoftExcel",
"position": [
448,
0
],
"parameters": {
"table": {
"__rl": true,
"mode": "list",
"value": "{6220E30B-55BD-614F-AA73-5C275D263361}",
"cachedResultUrl": "https://netorg17303936x-my.sharepoint.com/personal/vinay_optinext_ca/_layouts/15/Doc.aspx?sourcedoc=%7B3B366271-85B1-4FF7-9FE1-BDD145027E90%7D&file=CashARData.xlsx&action=default&mobileredirect=true&DefaultItemOpen=1&activeCell=Sheet1!A1:D51",
"cachedResultName": "Table1"
},
"filters": {},
"rawData": true,
"resource": "table",
"workbook": {
"__rl": true,
"mode": "list",
"value": "01WVQSKILRMI3DXMMF65HZ7YN52FCQE7UQ",
"cachedResultUrl": "https://netorg17303936x-my.sharepoint.com/personal/vinay_optinext_ca/_layouts/15/Doc.aspx?sourcedoc=%7B3B366271-85B1-4FF7-9FE1-BDD145027E90%7D&file=CashARData.xlsx&action=default&mobileredirect=true&DefaultItemOpen=1",
"cachedResultName": "CashARData"
},
"operation": "getRows",
"returnAll": true,
"worksheet": {
"__rl": true,
"mode": "list",
"value": "{A31DAD5C-F7E0-2A4B-B868-E4B2444E9398}",
"cachedResultUrl": "https://netorg17303936x-my.sharepoint.com/personal/vinay_optinext_ca/_layouts/15/Doc.aspx?sourcedoc=%7B3B366271-85B1-4FF7-9FE1-BDD145027E90%7D&file=CashARData.xlsx&action=default&mobileredirect=true&DefaultItemOpen=1&activeCell=Sheet1!A1",
"cachedResultName": "Sheet1"
}
},
"credentials": {
"microsoftExcelOAuth2Api": {
"id": "<Microsoft Account API KEY>",
"name": "Microsoft Excel account"
}
},
"typeVersion": 2.1
},
{
"id": "7c23ec26-e63b-4dfe-b82b-79b5a892c91d",
"name": "付箋5",
"type": "n8n-nodes-base.stickyNote",
"position": [
-560,
272
],
"parameters": {
"color": 2,
"width": 704,
"height": 176,
"content": "***Possible Enhancements***: \n\n1. Getting Invoice data from Data Table such as Snowflake, Databricks\n2. Getting Bank Statement from Bank accounts directly \n3. Posting the Data back to either ERP Systems or Data based with Matched Invoices to update the cash flow. "
},
"typeVersion": 1
}
],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "983d50ae-2007-4b12-9645-838649f3db28",
"connections": {
"8e42332e-f1cb-41f8-a0e1-f37b6ab3e75a": {
"main": [
[
{
"node": "464d6980-ee91-4509-b433-012adb2bcf88",
"type": "main",
"index": 0
}
]
]
},
"38449472-1724-44cc-aa6b-af80c8eaeb6b": {
"main": [
[
{
"node": "822968f4-1066-417d-9521-d1064f511bb7",
"type": "main",
"index": 0
}
]
]
},
"822968f4-1066-417d-9521-d1064f511bb7": {
"main": [
[
{
"node": "a8014539-db77-4ade-97c5-d42077119291",
"type": "main",
"index": 0
}
]
]
},
"a8014539-db77-4ade-97c5-d42077119291": {
"main": [
[
{
"node": "b9395a80-9e22-4e11-8e8e-85f558cc618f",
"type": "main",
"index": 0
}
]
]
},
"3a9cc7b1-aeeb-4f5f-b61f-db5fe3dfc402": {
"ai_languageModel": [
[
{
"node": "464d6980-ee91-4509-b433-012adb2bcf88",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"99f88b81-4e58-4a5d-a47a-67f6ba0771a4": {
"main": [
[]
]
},
"451c356d-2215-4c59-8f92-40678b080c2b": {
"main": [
[
{
"node": "38449472-1724-44cc-aa6b-af80c8eaeb6b",
"type": "main",
"index": 0
}
]
]
},
"b9395a80-9e22-4e11-8e8e-85f558cc618f": {
"main": [
[
{
"node": "8e42332e-f1cb-41f8-a0e1-f37b6ab3e75a",
"type": "main",
"index": 0
}
]
]
},
"464d6980-ee91-4509-b433-012adb2bcf88": {
"main": [
[
{
"node": "99f88b81-4e58-4a5d-a47a-67f6ba0771a4",
"type": "main",
"index": 0
}
]
]
}
}
}よくある質問
このワークフローの使い方は？
上記のJSON設定コードをコピーし、n8nインスタンスで新しいワークフローを作成して「JSONからインポート」を選択、設定を貼り付けて認証情報を必要に応じて変更してください。
このワークフローはどんな場面に適していますか？
中級 - コンテンツ作成, マルチモーダルAI
有料ですか？
このワークフローは完全無料です。ただし、ワークフローで使用するサードパーティサービス（OpenAI APIなど）は別途料金が発生する場合があります。
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