CUSTOMER SUPPORT

Refund Fraud: Correlate Stripe Radar Flags to Refund History

When Stripe flags a charge as elevated-risk, checks whether the same customer or payment method has open or recent refund requests in Postgres and Intercom.

CategoryCustomer Support
Enginesim
Difficultyintermediate
Triggerevent
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerStripe charge flagged elevated-riskStripeStripe
  • ActionQuery Postgres for related refund requestsPostgreSQLPostgres
  • LogicProceed only if Radar flag AND refund activity
  • ActionTag and hold Intercom conversationIntercomIntercom
  • ActionRecord fraud-review case in PostgresPostgreSQLPostgres
  • OutputAlert risk team in Microsoft TeamsMicrosoft Teams

What it does

Turns Stripe's own fraud signal into a refund-abuse early warning. When Radar rates a payment as risky, the flow looks backward at that customer's refund footprint — if they're also requesting refunds, that's a strong combined fraud signal worth holding.

When to use it

Use this when you already rely on Stripe Radar for payment fraud and want to connect it to your refund pipeline so card-testing and friendly-fraud rings that also abuse refunds surface in one place.

How it works

  1. 1A Stripe `charge.flagged` / elevated-risk event triggers the flow.
  2. 2Postgres is queried for any open or recent refund requests tied to the customer or payment fingerprint.
  3. 3A logic step requires both a Radar flag and refund activity to proceed; otherwise it exits quietly.
  4. 4When both fire, the matching Intercom conversation is tagged `fraud-review` and put on hold.
  5. 5A fraud-review case is recorded back in Postgres for audit.
  6. 6A Microsoft Teams message alerts the risk team with the correlated signals.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect StripeCustomers, subscriptions, payments.
  2. 2
    Connect PostgresAny Postgres URL — query, write, migrate.
  3. 3
    Connect IntercomConversations, contacts, articles.
  4. 4
    Connect Microsoft TeamsChannels, chats, files.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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