AI-powered fuzzy matching, and assigns confidence scores.

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1. Workflow Overview

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 r...

Best for

Document Extraction automation workflows
AI Summarization automation workflows
advanced n8n builders looking for reusable templates

Tools used

n8n-nodes-base.webhook, n8n-nodes-base.set, n8n-nodes-base.if, n8n-nodes-base.extractfromfile, n8n-nodes-base.merge, n8n-nodes-base.code, @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.lmchatopenai

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by ResilNext.

Original n8n.io source

1.1 Workflow description

Title: AI-powered fuzzy matching, and assigns confidence scores.
Workflow name: 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

Data Ingestion Receives financial data via webhook from different sources.
Source Detection & Routing Identifies the data type and routes it to the correct normalization flow.
Data Normalization Converts all records into a unified schema with consistent fields like ID, amount, date, and description.
Data Merging Combines all normalized records into a single dataset for matching.
Deterministic Matching Matches records using exact field combinations such as ID, amount, and date to generate initial confidence.
Match Quality Check Filters low-confidence matches for further analysis.
AI Fuzzy Matching Uses AI to identify near matches based on descriptions, amount tolerance, and date proximity.
Confidence Scoring Combines deterministic and AI results into a final confidence score with a detailed audit trail.
Decision Routing

High confidence → auto-reconciled
Low confidence → flagged for human review

Reporting Logs reconciliation results into Google Sheets.
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.

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - Webhook - Receive Financial Data

Type / Role: n8n-nodes-base.webhook - webhook
Config choices: Version 2.1

Block 2 - Workflow Configuration

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 3 - Check Data Source Type

Type / Role: n8n-nodes-base.if - if
Config choices: Version 2.3

Block 4 - Extract CSV Data

Type / Role: n8n-nodes-base.extractFromFile - extractFromFile
Config choices: Version 1.1

Block 5 - Normalize Bank Statement Schema

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 6 - Normalize Invoice Schema

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 7 - Normalize ERP Schema

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 8 - Normalize CSV Schema

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 9 - Merge All Normalized Data

Type / Role: n8n-nodes-base.merge - merge
Config choices: Version 3.2

Block 10 - Deterministic Matching Logic

Type / Role: n8n-nodes-base.code - code
Config choices: Version 2

Block 11 - Check Match Quality

Type / Role: n8n-nodes-base.if - if
Config choices: Version 2.3

Block 12 - AI Fuzzy Matching Agent

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 3

Block 13 - OpenAI Chat Model

Type / Role: @n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices: Version 1.3

Block 14 - Structured Output Parser

Type / Role: @n8n/n8n-nodes-langchain.outputParserStructured - outputParserStructured
Config choices: Version 1.3

Block 15 - Merge Matched Results

Type / Role: n8n-nodes-base.merge - merge
Config choices: Version 3.2

Block 16 - Calculate Confidence Scores

Type / Role: n8n-nodes-base.code - code
Config choices: Version 2

Block 17 - Route by Confidence Threshold

Type / Role: n8n-nodes-base.if - if
Config choices: Version 2.3

Block 18 - Flag for Human Review

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 19 - Mark as Auto-Reconciled

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 20 - Merge All Results

Type / Role: n8n-nodes-base.merge - merge
Config choices: Version 3.2

Block 21 - Log to Reconciliation Report

Type / Role: n8n-nodes-base.googleSheets - googleSheets
Config choices: Version 4.7

Block 22 - Notify Finance Team

Type / Role: n8n-nodes-base.slack - slack
Config choices: Version 2.4

Block 23 - Merge All Normalized Data1

Type / Role: n8n-nodes-base.merge - merge
Config choices: Version 3.2

Block 24 - Merge All Normalized Data2

Type / Role: n8n-nodes-base.merge - merge
Config choices: Version 3.2

Showing the first 24 of 34 workflow blocks. Download the JSON for the full node graph.

3. Summary Table

Workflow	AI-powered fuzzy matching, and assigns confidence scores.
Complexity	advanced
Nodes	34
Categories	Document Extraction, AI Summarization
Author	ResilNext
Published	23 Mar 2026

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/14276/14276.json as the source template for this automation.
2. Import the template into n8n

Open n8n, import the downloaded JSON, and review each node before activating the workflow.
3. Configure credentials and variables

Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
4. Test with sample data

Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
5. Activate and monitor

Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Download workflow JSON Original n8n.io source Document Extraction workflows AI Summarization workflows

Frequently asked questions

What does AI-powered fuzzy matching, and assigns confidence scores. do?

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Document Extraction, AI Summarization use case.

ResilNext

33 workflows

Nodes

n8n-nodes-base.webhook n8n-nodes-base.set n8n-nodes-base.if n8n-nodes-base.extractfromfile n8n-nodes-base.merge n8n-nodes-base.code @n8n/n8n-nodes-langchain.agent @n8n/n8n-nodes-langchain.lmchatopenai

Complexity

advanced

Published 23 Mar 2026

Likes 0

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Install path: /data/workflows/14276/14276.json

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AI-powered fuzzy matching, and assigns confidence scores.

Workflow preview

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

Overview

How It Works

Setup Instructions

Use Cases

Requirements

Notes

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - Webhook - Receive Financial Data

Block 2 - Workflow Configuration

Block 3 - Check Data Source Type

Block 4 - Extract CSV Data

Block 5 - Normalize Bank Statement Schema

Block 6 - Normalize Invoice Schema

Block 7 - Normalize ERP Schema

Block 8 - Normalize CSV Schema

Block 9 - Merge All Normalized Data

Block 10 - Deterministic Matching Logic

Block 11 - Check Match Quality

Block 12 - AI Fuzzy Matching Agent

Block 13 - OpenAI Chat Model

Block 14 - Structured Output Parser

Block 15 - Merge Matched Results

Block 16 - Calculate Confidence Scores

Block 17 - Route by Confidence Threshold

Block 18 - Flag for Human Review

Block 19 - Mark as Auto-Reconciled

Block 20 - Merge All Results

Block 21 - Log to Reconciliation Report

Block 22 - Notify Finance Team

Block 23 - Merge All Normalized Data1

Block 24 - Merge All Normalized Data2

3. Summary Table

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

2. Import the template into n8n

3. Configure credentials and variables

4. Test with sample data

5. Activate and monitor

5. General Notes & Resources

Frequently asked questions