CUSTOMER SUPPORT

Refund Fraud: Weekly Ring-Cluster Report to Notion

Weekly, clusters refund requests in Postgres by shared payment fingerprint, address, and device to surface coordinated fraud rings.

CategoryCustomer Support
Enginesim
Difficultyadvanced
Triggerschedule
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly schedule
  • ActionCluster refunds by shared signals in PostgresPostgreSQLPostgres
  • LogicFilter to significant clusters and rank by total
  • ActionCreate ranked cluster report in NotionNotionNotion
  • OutputPost top-rings summary and link to SlackSlack

What it does

Finds the rings, not just the individuals. Once a week it groups historical refunds by shared signals — card fingerprint, shipping address, device, email pattern — to reveal clusters of accounts working together, then ranks them by total dollars refunded so investigators tackle the biggest losses first.

When to use it

Run this for periodic fraud-investigation work when you want a strategic, ranked view of organized refund abuse rather than per-ticket alerts. It complements the real-time gates by catching slow-burn rings that individually stay under thresholds.

How it works

  1. 1A weekly schedule starts the report.
  2. 2Postgres runs a clustering query that groups refunds by shared fingerprint, address, and device signals.
  3. 3A logic step keeps only clusters above a member-count and dollar threshold and ranks them by total refunded.
  4. 4A Notion page is created with each ranked cluster, its members, and shared signals for the investigation log.
  5. 5A Slack summary links the report and highlights the top three rings of the week.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect PostgresAny Postgres URL — query, write, migrate.
  2. 2
    Connect NotionPages, databases, comments.
  3. 3
    Connect SlackChannels, DMs, threads, mentions.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

Run this workflow in your colony.

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