E-commerce Refund Risk Scoring Agent
New e-commerce orders are automatically scored for refund risk by an AI agent that logs results to Sheets and alerts teams via Discord or...
What it does
You instantly identify refund and chargeback risks for new orders so your team can act before losses occur. This agent analyzes incoming transactions, assigns clear risk scores to Google Sheets, and sends immediate alerts via Discord or Gmail whenever high-risk cases are detected.
Step by step
- Set up a webhook endpoint using FastAPI or Flask to receive POST requests from your e-commerce platform.
- Implement a deduplication step by checking for unique transaction IDs against a local cache or database before processing.
- Configure Google Sheets API credentials and define the spreadsheet structure with columns for order ID, risk score, and timestamp.
Where the LLM does the work
- Craft system prompts that instruct the LLM to analyze normalized transaction data and output JSON containing specific risk factors like shipping address mismatch or high-value items.
- Design few-shot examples within the prompt to demonstrate how the LLM should weigh conflicting signals such as new accounts versus verified payment methods.
Watch out for
Webhook payloads may contain sensitive PII, so strip or hash personally identifiable information before sending data to the OpenAI API.
Tools that fit
Webhook API Ingest incoming order payloads
The agent brief
Everything your agent needs, including the gotchas. Copy it and go.
You are helping me build the following AI agent workflow. ## Goal E-commerce Refund Risk Scoring Agent: New e-commerce orders are automatically scored for refund risk by an AI agent that logs results to Sheets and alerts teams via Discord or... ## Specification - What it does: You instantly identify refund and chargeback risks for new orders so your team can act before losses occur. This agent analyzes incoming transactions, assigns clear risk scores to Google Sheets, and sends immediate alerts via Discord or Gmail whenever high-risk cases are detected. - Trigger: Triggered by an event (Event · on new order webhook) - Autonomy: Fully hands-off - Expected setup effort: about an afternoon - Tools/services involved: - Webhook: Ingest incoming order payloads - OpenAI: AI risk scoring and classification - Google Sheets: Log results and deduplication check - Discord: Send real-time high-risk alerts - Gmail: Email finance stakeholders with risk context ## Known pitfalls, handle each one explicitly in your implementation 1. Webhook payloads may contain sensitive PII, so strip or hash personally identifiable information before sending data to the OpenAI API. ## Reference implementation https://n8n.io/workflows/12576 (workflow template) Fetch and inspect this before building. If it matches my stack, adapt it; if not, rebuild the pattern with my tools. ## Process requirements 1. Before building: ask me which of the listed tools I actually use and what my platform is (n8n / Make / code / other). Do not assume. 2. Adapt the pattern to my answers; do not force the reference stack. 3. Address every pitfall above; tell me how you handled each. 4. Provide a test plan I can run before letting this touch real data. 5. Ask before any step that sends messages, modifies data, or spends money. Source: https://usecasesforagents.com/use-case/e-commerce-refund-risk-scoring-agent/ via usecasesforagents.com
Frequently asked questions
Can I use a different LLM than OpenAI?
Yes. OpenAI is only the example LLM in this recipe. The same flow works with Anthropic, Cohere, DeepSeek and Google Gemini. Swap the LLM connection and keep the rest of the setup as written.
Want this running in your business?
This is what I do. I design and build AI agents like this one, and keep them running. If you want it set up for your team instead of doing it yourself, get in touch.
Who it's for
Also fits E-commerce Operator.
Finance teams can run the same extraction-and-file pattern on their own paperwork.