CUSTOMER SUPPORT

Refund Velocity Guard: Auto-Hold High-Frequency Refunders

On a schedule, scans Postgres for accounts that have requested more than N refunds in a rolling window and automatically suspends their refund eligibility.

CategoryCustomer Support
Enginesim
Difficultybeginner
Triggerschedule
Steps5
Setup~5 min

How it runs

The automated pipeline, trigger to output.

  • TriggerDaily schedule
  • ActionAggregate per-account refund counts in PostgresPostgreSQLPostgres
  • LogicCompare against count and amount thresholds
  • ActionWrite refund-block flag to eligibility tablePostgreSQLPostgres
  • OutputPost throttled-accounts digest to SlackSlack

What it does

Catches refund abuse by velocity rather than by linkage: any account that crosses a refund-frequency threshold inside a rolling window gets its refund eligibility frozen and is queued for manual review. This stops a single account from draining refunds even when no obvious linkage exists.

When to use it

Use this as a always-on backstop when you want a deterministic, policy-driven cap on how many refunds one account can pull before a human looks. Good for teams that prefer a clear rule ("more than 3 refunds in 30 days = hold") over case-by-case judgment.

How it works

  1. 1A daily schedule kicks off the scan.
  2. 2Postgres aggregates refund counts and dollar totals per account over the rolling 30-day window.
  3. 3A logic step compares each account against the configured count and amount thresholds.
  4. 4For accounts over the line, a flag is written back to the Postgres eligibility table to block further auto-refunds.
  5. 5A Slack digest lists every newly throttled account with its refund count, total, and the rule it tripped.

Set it up

What you configure once, before turning it on.

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

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