HumationHuman + Automation
Back to Catalog

Detect and score refund risk with Webhook, OpenAI and Google Sheets

Avkash KakdiyaAvkash Kakdiya
5 views
2/3/2026
Pub/SubAutomationGoogle CloudHTTP RequestEvent-Driven
Official Page

How it works

This workflow automatically evaluates refund and chargeback risk for incoming e-commerce orders. Orders are received via a webhook, processed individually, and checked to avoid duplicate analysis. Each transaction is normalized and sent to OpenAI for structured risk scoring and classification. Results are logged for auditing, alerts are triggered for high-risk cases, and processed orders are marked to prevent reprocessing.

Step-by-step

  • Step 1 – Ingest incoming orders

    • Webhook – Receives single or bulk order payloads from external systems.
    • Split Out – Breaks array-based payloads into individual order records.
    • Split In Batches – Iterates through each order in a controlled loop.

  • Step 2 – Deduplication check

    • IF (DEDUPE CHECK) – Verifies whether an order was already processed and skips duplicates.

  • Step 3 – Normalize transaction data

    • Code (Normalize Data) – Validates required fields and standardizes order, customer, and behavioral attributes.

  • Step 4 – AI risk assessment

    • OpenAI (Message a model) – Sends normalized transaction data to the AI model and requests a strict JSON risk evaluation.

  • Step 5 – Parse AI output

    • Code (Parse AI Output) – Cleans the AI response and extracts risk score, risk level, key drivers, and recommendations.

  • Step 6 – Log results

    • Google Sheets (Append) – Stores timestamps, order details, and AI risk outcomes for reporting and audits.

  • Step 7 – Risk decision and alerts

    • IF (High Risk) – Filters only transactions classified as HIGH risk.
    • Discord – Sends real-time alerts to operations or finance teams.
    • Gmail – Emails finance stakeholders with full risk context.

  • Step 8 – Mark order as processed

    • Google Sheets (Update) – Updates the source row to prevent duplicate processing.

Why use this?

  • Automatically detects high refund or chargeback risk before losses occur.
  • Eliminates manual review with consistent, AI-driven risk scoring.
  • Sends instant alerts so teams can act quickly on high-risk orders.
  • Maintains a clear audit trail for compliance and reporting.
  • Scales easily to handle single or bulk order evaluations.

n8n Workflow: Detect and Score Refund Risk

This n8n workflow provides a robust system for detecting and scoring refund risk based on incoming data, leveraging OpenAI for analysis and Google Sheets for record-keeping. It also includes notification capabilities via Discord and Gmail for high-risk cases.

What it does

This workflow automates the following steps:

  1. Receives Data: It starts by listening for incoming data via a webhook, which is expected to contain information relevant to a potential refund (e.g., customer details, order information, reason for refund).
  2. Prepares Data for AI Analysis: The received data is then processed by a "Code" node to format it into a prompt suitable for OpenAI. This likely involves extracting key fields and structuring them into a coherent question or statement for risk assessment.
  3. Analyzes Refund Risk with OpenAI: The formatted prompt is sent to OpenAI, which acts as an AI assistant to analyze the provided information and assess the refund risk. It's expected to return a risk score or a textual assessment.
  4. Extracts Risk Score: Another "Code" node processes OpenAI's response to extract a numerical risk score from the AI's output.
  5. Logs Data to Google Sheets: The original data, along with the OpenAI response and the extracted risk score, is appended as a new row in a specified Google Sheet for record-keeping and further analysis.
  6. Evaluates Risk Threshold: An "If" node checks if the extracted risk score exceeds a predefined threshold (e.g., a high-risk score).
  7. Notifies High-Risk Cases:
    • If the risk score is high, it sends a notification to a Discord channel, alerting a team to a potential high-risk refund.
    • Concurrently, it sends an email via Gmail to a designated recipient(s) for the same high-risk refund.
  8. Handles Low-Risk Cases: If the risk score is below the threshold, the workflow simply proceeds without sending specific high-risk notifications.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running n8n instance.
  • OpenAI API Key: An API key for OpenAI to access its language models for risk assessment.
  • Google Sheets Account: Access to a Google Sheets spreadsheet where refund data and risk scores will be logged. You'll need to configure credentials for n8n to write to your sheet.
  • Discord Account/Webhook: A Discord server and a webhook URL configured to receive notifications for high-risk refunds.
  • Gmail Account: A Gmail account configured as a credential in n8n to send email notifications.

Setup/Usage

  1. Import the Workflow: Import the provided JSON into your n8n instance.
  2. Configure Credentials:
    • OpenAI: Add your OpenAI API key as a credential in n8n.
    • Google Sheets: Set up a Google Sheets credential (e.g., OAuth2) to allow n8n to write to your spreadsheet.
    • Discord: Configure a Discord credential, likely using a webhook URL.
    • Gmail: Set up a Gmail credential (e.g., OAuth2) for sending emails.
  3. Customize Nodes:
    • Webhook: Activate the Webhook node and copy its URL. This is where your external system will send refund data.
    • Code (Prepare OpenAI Prompt): Review and adjust the JavaScript code to correctly format your incoming webhook data into a prompt for OpenAI.
    • OpenAI: Configure the OpenAI node with the desired model and any specific parameters for risk assessment.
    • Code (Extract Risk Score): Adjust the JavaScript code to accurately parse the risk score from OpenAI's response.
    • Google Sheets: Specify the Spreadsheet ID and Sheet Name where you want to log the data. Ensure the column headers in your sheet match the data being sent.
    • If: Set the condition for the "If" node to define what constitutes a "high-risk" score.
    • Discord: Configure the Discord node with your webhook URL and customize the message content for high-risk alerts.
    • Gmail: Configure the Gmail node with the recipient email addresses, subject, and body for high-risk alerts.
  4. Activate the Workflow: Once all configurations are complete, activate the workflow.
  5. Send Test Data: Send a test request to the Webhook URL with sample refund data to ensure the workflow processes correctly and notifications are sent as expected.

Related Templates

Track competitor SEO keywords with Decodo + GPT-4.1-mini + Google Sheets

This workflow automates competitor keyword research using OpenAI LLM and Decodo for intelligent web scraping. Who this is for SEO specialists, content strategists, and growth marketers who want to automate keyword research and competitive intelligence. Marketing analysts managing multiple clients or websites who need consistent SEO tracking without manual data pulls. Agencies or automation engineers using Google Sheets as an SEO data dashboard for keyword monitoring and reporting. What problem this workflow solves Tracking competitor keywords manually is slow and inconsistent. Most SEO tools provide limited API access or lack contextual keyword analysis. This workflow solves that by: Automatically scraping any competitor’s webpage with Decodo. Using OpenAI GPT-4.1-mini to interpret keyword intent, density, and semantic focus. Storing structured keyword insights directly in Google Sheets for ongoing tracking and trend analysis. What this workflow does Trigger — Manually start the workflow or schedule it to run periodically. Input Setup — Define the website URL and target country (e.g., https://dev.to, france). Data Scraping (Decodo) — Fetch competitor web content and metadata. Keyword Analysis (OpenAI GPT-4.1-mini) Extract primary and secondary keywords. Identify focus topics and semantic entities. Generate a keyword density summary and SEO strength score. Recommend optimization and internal linking opportunities. Data Structuring — Clean and convert GPT output into JSON format. Data Storage (Google Sheets) — Append structured keyword data to a Google Sheet for long-term tracking. Setup Prerequisites If you are new to Decode, please signup on this link visit.decodo.com n8n account with workflow editor access Decodo API credentials OpenAI API key Google Sheets account connected via OAuth2 Make sure to install the Decodo Community node. Create a Google Sheet Add columns for: primarykeywords, seostrengthscore, keyworddensity_summary, etc. Share with your n8n Google account. Connect Credentials Add credentials for: Decodo API credentials - You need to register, login and obtain the Basic Authentication Token via Decodo Dashboard OpenAI API (for GPT-4o-mini) Google Sheets OAuth2 Configure Input Fields Edit the “Set Input Fields” node to set your target site and region. Run the Workflow Click Execute Workflow in n8n. View structured results in your connected Google Sheet. How to customize this workflow Track Multiple Competitors → Use a Google Sheet or CSV list of URLs; loop through them using the Split In Batches node. Add Language Detection → Add a Gemini or GPT node before keyword analysis to detect content language and adjust prompts. Enhance the SEO Report → Expand the GPT prompt to include backlink insights, metadata optimization, or readability checks. Integrate Visualization → Connect your Google Sheet to Looker Studio for SEO performance dashboards. Schedule Auto-Runs → Use the Cron Node to run weekly or monthly for competitor keyword refreshes. Summary This workflow automates competitor keyword research using: Decodo for intelligent web scraping OpenAI GPT-4.1-mini for keyword and SEO analysis Google Sheets for live tracking and reporting It’s a complete AI-powered SEO intelligence pipeline ideal for teams that want actionable insights on keyword gaps, optimization opportunities, and content focus trends, without relying on expensive SEO SaaS tools.

Ranjan DailataBy Ranjan Dailata
161

Generate song lyrics and music from text prompts using OpenAI and Fal.ai Minimax

Spark your creativity instantly in any chat—turn a simple prompt like "heartbreak ballad" into original, full-length lyrics and a professional AI-generated music track, all without leaving your conversation. 📋 What This Template Does This chat-triggered workflow harnesses AI to generate detailed, genre-matched song lyrics (at least 600 characters) from user messages, then queues them for music synthesis via Fal.ai's minimax-music model. It polls asynchronously until the track is ready, delivering lyrics and audio URL back in chat. Crafts original, structured lyrics with verses, choruses, and bridges using OpenAI Submits to Fal.ai for melody, instrumentation, and vocals aligned to the style Handles long-running generations with smart looping and status checks Returns complete song package (lyrics + audio link) for seamless sharing 🔧 Prerequisites n8n account (self-hosted or cloud with chat integration enabled) OpenAI account with API access for GPT models Fal.ai account for AI music generation 🔑 Required Credentials OpenAI API Setup Go to platform.openai.com → API keys (sidebar) Click "Create new secret key" → Name it (e.g., "n8n Songwriter") Copy the key and add to n8n as "OpenAI API" credential type Test by sending a simple chat completion request Fal.ai HTTP Header Auth Setup Sign up at fal.ai → Dashboard → API Keys Generate a new API key → Copy it In n8n, create "HTTP Header Auth" credential: Name="Fal.ai", Header Name="Authorization", Header Value="Key [Your API Key]" Test with a simple GET to their queue endpoint (e.g., /status) ⚙️ Configuration Steps Import the workflow JSON into your n8n instance Assign OpenAI API credentials to the "OpenAI Chat Model" node Assign Fal.ai HTTP Header Auth to the "Generate Music Track", "Check Generation Status", and "Fetch Final Result" nodes Activate the workflow—chat trigger will appear in your n8n chat interface Test by messaging: "Create an upbeat pop song about road trips" 🎯 Use Cases Content Creators: YouTubers generating custom jingles for videos on the fly, streamlining production from idea to audio export Educators: Music teachers using chat prompts to create era-specific folk tunes for classroom discussions, fostering interactive learning Gift Personalization: Friends crafting anniversary R&B tracks from shared memories via quick chats, delivering emotional audio surprises Artist Brainstorming: Songwriters prototyping hip-hop beats in real-time during sessions, accelerating collaboration and iteration ⚠️ Troubleshooting Invalid JSON from AI Agent: Ensure the system prompt stresses valid JSON; test the agent standalone with a sample query Music Generation Fails (401/403): Verify Fal.ai API key has minimax-music access; check usage quotas in dashboard Status Polling Loops Indefinitely: Bump wait time to 45-60s for complex tracks; inspect fal.ai queue logs for bottlenecks Lyrics Under 600 Characters: Tweak agent prompt to enforce fuller structures like [V1][C][V2][B][C]; verify output length in executions

Daniel NkenchoBy Daniel Nkencho
601

Automate invoice processing with OCR, GPT-4 & Salesforce opportunity creation

PDF Invoice Extractor (AI) End-to-end pipeline: Watch Drive ➜ Download PDF ➜ OCR text ➜ AI normalize to JSON ➜ Upsert Buyer (Account) ➜ Create Opportunity ➜ Map Products ➜ Create OLI via Composite API ➜ Archive to OneDrive. --- Node by node (what it does & key setup) 1) Google Drive Trigger Purpose: Fire when a new file appears in a specific Google Drive folder. Key settings: Event: fileCreated Folder ID: google drive folder id Polling: everyMinute Creds: googleDriveOAuth2Api Output: Metadata { id, name, ... } for the new file. --- 2) Download File From Google Purpose: Get the file binary for processing and archiving. Key settings: Operation: download File ID: ={{ $json.id }} Creds: googleDriveOAuth2Api Output: Binary (default key: data) and original metadata. --- 3) Extract from File Purpose: Extract text from PDF (OCR as needed) for AI parsing. Key settings: Operation: pdf OCR: enable for scanned PDFs (in options) Output: JSON with OCR text at {{ $json.text }}. --- 4) Message a model (AI JSON Extractor) Purpose: Convert OCR text into strict normalized JSON array (invoice schema). Key settings: Node: @n8n/n8n-nodes-langchain.openAi Model: gpt-4.1 (or gpt-4.1-mini) Message role: system (the strict prompt; references {{ $json.text }}) jsonOutput: true Creds: openAiApi Output (per item): $.message.content → the parsed JSON (ensure it’s an array). --- 5) Create or update an account (Salesforce) Purpose: Upsert Buyer as Account using an external ID. Key settings: Resource: account Operation: upsert External Id Field: taxid_c External Id Value: ={{ $json.message.content.buyer.tax_id }} Name: ={{ $json.message.content.buyer.name }} Creds: salesforceOAuth2Api Output: Account record (captures Id) for downstream Opportunity. --- 6) Create an opportunity (Salesforce) Purpose: Create Opportunity linked to the Buyer (Account). Key settings: Resource: opportunity Name: ={{ $('Message a model').item.json.message.content.invoice.code }} Close Date: ={{ $('Message a model').item.json.message.content.invoice.issue_date }} Stage: Closed Won Amount: ={{ $('Message a model').item.json.message.content.summary.grand_total }} AccountId: ={{ $json.id }} (from Upsert Account output) Creds: salesforceOAuth2Api Output: Opportunity Id for OLI creation. --- 7) Build SOQL (Code / JS) Purpose: Collect unique product codes from AI JSON and build a SOQL query for PricebookEntry by Pricebook2Id. Key settings: pricebook2Id (hardcoded in script): e.g., 01sxxxxxxxxxxxxxxx Source lines: $('Message a model').first().json.message.content.products Output: { soql, codes } --- 8) Query PricebookEntries (Salesforce) Purpose: Fetch PricebookEntry.Id for each Product2.ProductCode. Key settings: Resource: search Query: ={{ $json.soql }} Creds: salesforceOAuth2Api Output: Items with Id, Product2.ProductCode (used for mapping). --- 9) Code in JavaScript (Build OLI payloads) Purpose: Join lines with PBE results and Opportunity Id ➜ build OpportunityLineItem payloads. Inputs: OpportunityId: ={{ $('Create an opportunity').first().json.id }} Lines: ={{ $('Message a model').first().json.message.content.products }} PBE rows: from previous node items Output: { body: { allOrNone:false, records:[{ OpportunityLineItem... }] } } Notes: Converts discount_total ➜ per-unit if needed (currently commented for standard pricing). Throws on missing PBE mapping or empty lines. --- 10) Create Opportunity Line Items (HTTP Request) Purpose: Bulk create OLIs via Salesforce Composite API. Key settings: Method: POST URL: https://<your-instance>.my.salesforce.com/services/data/v65.0/composite/sobjects Auth: salesforceOAuth2Api (predefined credential) Body (JSON): ={{ $json.body }} Output: Composite API results (per-record statuses). --- 11) Update File to One Drive Purpose: Archive the original PDF in OneDrive. Key settings: Operation: upload File Name: ={{ $json.name }} Parent Folder ID: onedrive folder id Binary Data: true (from the Download node) Creds: microsoftOneDriveOAuth2Api Output: Uploaded file metadata. --- Data flow (wiring) Google Drive Trigger → Download File From Google Download File From Google → Extract from File → Update File to One Drive Extract from File → Message a model Message a model → Create or update an account Create or update an account → Create an opportunity Create an opportunity → Build SOQL Build SOQL → Query PricebookEntries Query PricebookEntries → Code in JavaScript Code in JavaScript → Create Opportunity Line Items --- Quick setup checklist 🔐 Credentials: Connect Google Drive, OneDrive, Salesforce, OpenAI. 📂 IDs: Drive Folder ID (watch) OneDrive Parent Folder ID (archive) Salesforce Pricebook2Id (in the JS SOQL builder) 🧠 AI Prompt: Use the strict system prompt; jsonOutput = true. 🧾 Field mappings: Buyer tax id/name → Account upsert fields Invoice code/date/amount → Opportunity fields Product name must equal your Product2.ProductCode in SF. ✅ Test: Drop a sample PDF → verify: AI returns array JSON only Account/Opportunity created OLI records created PDF archived to OneDrive --- Notes & best practices If PDFs are scans, enable OCR in Extract from File. If AI returns non-JSON, keep “Return only a JSON array” as the last line of the prompt and keep jsonOutput enabled. Consider adding validation on parsing.warnings to gate Salesforce writes. For discounts/taxes in OLI: Standard OLI fields don’t support per-line discount amounts directly; model them in UnitPrice or custom fields. Replace the Composite API URL with your org’s domain or use the Salesforce node’s Bulk Upsert for simplicity.

Le NguyenBy Le Nguyen
942