Vinay Gangidi This workflow corresponds to n8n.io template #8936 — we link there as the canonical source.
This workflow follows the Agent → OpenAI Chat recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"id": "n4bNr0cnmlkuN8fy",
"meta": {
"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": "When clicking \u2018Execute workflow\u2019",
"type": "n8n-nodes-base.manualTrigger",
"position": [
224,
0
],
"parameters": {},
"typeVersion": 1
},
{
"id": "8e42332e-f1cb-41f8-a0e1-f37b6ab3e75a",
"name": "Extract text",
"type": "n8n-nodes-base.mistralAi",
"position": [
1344,
0
],
"parameters": {
"options": {
"batch": false
},
"binaryProperty": "Data"
},
"credentials": {
"mistralCloudApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "a8014539-db77-4ade-97c5-d42077119291",
"name": "Get Bank Statement",
"type": "n8n-nodes-base.microsoftOneDrive",
"position": [
896,
0
],
"parameters": {
"fileId": "01WVQSKIIAS4II25G37JGK6QHSYCDROS76",
"operation": "get"
},
"credentials": {
"microsoftOneDriveOAuth2Api": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "822968f4-1066-417d-9521-d1064f511bb7",
"name": "Code in JavaScript",
"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": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "99f88b81-4e58-4a5d-a47a-67f6ba0771a4",
"name": "Get Transaction, Matches, Summary",
"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": "Extract the Data from Unstructured File",
"type": "n8n-nodes-base.microsoftOneDrive",
"position": [
1120,
0
],
"parameters": {
"fileId": "={{ $json.id }}",
"operation": "download",
"binaryPropertyName": "=Data"
},
"credentials": {
"microsoftOneDriveOAuth2Api": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "464d6980-ee91-4509-b433-012adb2bcf88",
"name": "Process the Invoice Vs Bank Statement Data",
"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 \u00b12 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\u20131) 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": "Sticky Note",
"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 \u2013 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 \u2013 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": "Sticky Note1",
"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": "Sticky Note2",
"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": "Sticky Note3",
"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 \u2192 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": "Sticky Note4",
"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": "Get Invoice Data",
"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": {
"name": "<your credential>"
}
},
"typeVersion": 2.1
},
{
"id": "7c23ec26-e63b-4dfe-b82b-79b5a892c91d",
"name": "Sticky Note5",
"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,
"settings": {
"executionOrder": "v1"
},
"versionId": "983d50ae-2007-4b12-9645-838649f3db28",
"connections": {
"Extract text": {
"main": [
[
{
"node": "Process the Invoice Vs Bank Statement Data",
"type": "main",
"index": 0
}
]
]
},
"Get Invoice Data": {
"main": [
[
{
"node": "Code in JavaScript",
"type": "main",
"index": 0
}
]
]
},
"Code in JavaScript": {
"main": [
[
{
"node": "Get Bank Statement",
"type": "main",
"index": 0
}
]
]
},
"Get Bank Statement": {
"main": [
[
{
"node": "Extract the Data from Unstructured File",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Chat Model1": {
"ai_languageModel": [
[
{
"node": "Process the Invoice Vs Bank Statement Data",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Get Transaction, Matches, Summary": {
"main": [
[]
]
},
"When clicking \u2018Execute workflow\u2019": {
"main": [
[
{
"node": "Get Invoice Data",
"type": "main",
"index": 0
}
]
]
},
"Extract the Data from Unstructured File": {
"main": [
[
{
"node": "Extract text",
"type": "main",
"index": 0
}
]
]
},
"Process the Invoice Vs Bank Statement Data": {
"main": [
[
{
"node": "Get Transaction, Matches, Summary",
"type": "main",
"index": 0
}
]
]
}
}
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
microsoftExcelOAuth2ApimicrosoftOneDriveOAuth2ApimistralCloudApiopenAiApi
Pro
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About this workflow
Cash Reconciliation with AI
Source: https://n8n.io/workflows/8936/ — original creator credit. Request a take-down →
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