How to Use AI to Detect Potentially Fraudulent Orders

Flag suspicious orders automatically using AI analysis before they ship.

What This Integration Does

Card fraud and friendly fraud are expensive: chargebacks, lost product, and shipping costs you never recover. Manually reviewing every order doesn't scale, and basic rules (address mismatch, high cart value) catch the obvious cases but miss patterns that only become clear when you read several signals at once. This workflow puts an AI Agent in front of fulfillment so every order is risk-scored before a label is ever generated.

It runs on a webhook from your store the moment an order is placed. The order payload is enriched with customer history and your fraud policy, an AI Agent scores it as low, medium, or high risk, and a Condition node fans out to three paths: low risk continues to fulfillment, medium risk drops onto a manual review board, and high risk freezes the order and pings on-call. The original order is left untouched in the store so you can cancel cleanly if review confirms fraud.

Prerequisites

A Shopify, BigCommerce, or WooCommerce connection with read access to orders and customers, plus the ability to cancel an order.
A Slack connection for on-call alerts (channel like #fraud-review).
A Monday.com connection with a board for manual review (or a substitute task tracker).
A Knowledge Collection containing your fraud policy and a small set of labelled past examples.
An LLM provider configured on the workspace so the AI Agent node can run.

Step 1: Webhook Trigger on New Order

Drop a Trigger node onto the canvas and set its type to Webhook. Register the webhook URL in your store's order-created event (Shopify: orders/create, WooCommerce: order.created, BigCommerce: store/order/created). The trigger fires before fulfillment, which is what gives you a window to intervene.

Step 2: Enrich the Order with Customer History

Add a Connector node pointing at your store and pull the customer record - Shopify get-customer, BigCommerce get-customer, or WooCommerce get-customer. You want lifetime order count, prior chargebacks, and account age. Follow it with a Connector call to list-orders filtered by that customer's email so the agent can see velocity (how many orders in the last 24 hours).

Step 3: Query the Knowledge Base for Policy

Add a Knowledge node and query the fraud policy collection with the order's key signals (country, total, billing/shipping match). The retrieved snippets become part of the prompt so the agent's reasoning matches your house rules instead of generic LLM defaults.

Step 4: AI Agent Risk Scoring

Add an AI Agent node. Wire the order JSON, customer record, prior orders, and knowledge snippets as inputs. Force a structured output schema so downstream nodes can branch reliably:

{
  "riskLevel": "low | medium | high",
  "riskFactors": ["string"],
  "recommendation": "fulfill | review | hold",
  "confidence": 0.0
}

System prompt: "You are a fraud analyst. Score this order using the provided policy and customer history. Consider billing vs shipping match, email domain reputation, velocity, value relative to customer history, and shipping to forwarder addresses. Return only the schema." Pass the order under {{ trigger.body }}, the customer under {{ step.customer }}, prior orders under {{ step.priorOrders }}, and policy snippets under {{ step.policy }}.

Step 5: Route by Risk Level

Add a Condition node on {{ step.agent.riskLevel }} with three branches:

low: route straight into your existing fulfillment subworkflow via a Subworkflow node.
medium: Connector node on monday with create-item, pushing the order ID, agent reasoning, and risk factors onto a review board.
high: Connector node on shopify with cancel-order (or your equivalent), followed by a slack send-message call into #fraud-review.

Step 6: Audit Trail and Feedback Loop

Append every agent decision (input snapshot, risk level, factors, final outcome) to a mongodb collection with insert-documents. Once a week, label any disputed or confirmed-fraud orders and use them to update the Knowledge Collection - this is how the agent gets better at your specific fraud patterns over time.

Tips

Always force a JSON schema on the agent output. Free-form text leads to fragile downstream parsing.
Keep the prompt short and put the volatile signals (order data, history) in inputs, not the prompt body - it makes A/B testing prompts cleaner.
Start with the agent in shadow mode: log its recommendation but always fulfill. After a week, compare its calls against your manual reviews before letting it block orders.
Use confidence from the schema to pull low-confidence "low" verdicts onto the review board anyway.

Common Pitfalls

Webhook race conditions: stores sometimes fire orders/create before the payment is captured. Check the financial status field before scoring.
Address normalization: "123 Main St" vs "123 Main Street" will look mismatched to a naive prompt. Either normalize in a Transform step or instruct the agent to ignore formatting differences.
Cancelling already-fulfilled orders: if your store's flow auto-fulfills on payment, the cancel call will fail. Make sure the workflow runs before any other automation, or route through a Subworkflow that gates fulfillment.
LLM cost creep: scoring every order adds up. Skip the agent entirely (Condition node up front) for trusted repeat customers with N+ prior clean orders.

Testing

Place a small test order on your store with a deliberately mismatched billing/shipping address and a fresh email. Watch the workflow run end-to-end and confirm the order shows up on your review board with the right risk factors listed. Then place a normal order from a known customer and confirm it routes to fulfillment. Only enable the high-risk cancel branch after both paths have been verified.