FINANCE

Monthly card spend policy rollup to BigQuery

At month end it aggregates all Stripe Issuing spend by category, cardholder, and policy compliance, loads the dataset into BigQuery, and posts the violation rate trend to Slack.

CategoryFinance
Enginesim
Difficultyintermediate
Triggerschedule
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerFirst-of-month schedule
  • ActionPull prior month's Stripe Issuing transactionsStripeStripe
  • LogicClassify and aggregate by category, cardholder, compliance
  • ActionLoad aggregated rows into BigQueryGoogle BigQueryBigQuery
  • OutputPost violation-rate trend to SlackSlack

What it does

Produces a month-end analytics dataset from your corporate-card activity. It pulls the full month of Stripe Issuing transactions, classifies each against the spend policy, and aggregates totals by merchant category, cardholder, and compliant-versus-violation status. The structured result is loaded into BigQuery so finance can chart spend trends and policy violation rates over time.

When to use it

Reach for this when you need analytics, not enforcement: tracking which categories drive spend, which teams trend toward violations, and whether your policy tuning is working month over month. It feeds dashboards rather than blocking cards.

How it works

  1. 1A scheduled trigger fires on the first of the month.
  2. 2The flow pulls all Stripe Issuing transactions from the prior calendar month.
  3. 3A logic step classifies each transaction by category and compliance status, then aggregates totals.
  4. 4The aggregated rows are loaded into a BigQuery table partitioned by month.
  5. 5A query computes the period's violation rate versus the prior month.
  6. 6The trend and top spend categories are posted to the finance Slack channel.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect StripeCustomers, subscriptions, payments.
  2. 2
    Connect BigQueryDatasets, queries, schemas.
  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.

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