CHATBOTS
Intercom Return Fraud Flag and Hold
Scores incoming Intercom return requests against the customer's refund history in Stripe and Postgres.
How it runs
The automated pipeline, trigger to output.
- TriggerIntercom return request receivedIntercom
- ActionQuery refund count and return frequency from PostgresPostgres
- ActionCheck refund and dispute ratio in StripeStripe
- LogicCompute risk score against threshold
- OutputHold and route high-risk cases via Intercom note and SlackSlack
What it does
Adds a fraud gate to the return pipeline. Before any return is approved, it checks how many refunds the customer has already received, looks for serial-return patterns, and flags suspicious requests so a human reviews them while clean requests flow through normally.
When to use it
Use it when return abuse is rising and your automated RMA approvals have no risk check. It protects margin without slowing down legitimate customers.
How it works
- 1An Intercom return request triggers the flow.
- 2The customer's lifetime refund count and recent return frequency are queried from Postgres.
- 3Stripe is checked for the share of this customer's charges that ended in refunds or disputes.
- 4A logic step computes a risk score and compares it against your threshold.
- 5High-risk requests are placed on hold and an Intercom note plus a Slack alert send the case to the manual review queue; low-risk requests are tagged cleared to continue.
Set it up
What you configure once, before turning it on.
- 1Connect IntercomConversations, contacts, articles.
- 2Connect PostgresAny Postgres URL — query, write, migrate.
- 3Connect StripeCustomers, subscriptions, payments.
- 4Connect SlackChannels, DMs, threads, mentions.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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