FINANCE

Audit aging deferred-revenue balances that should have recognized

Weekly, scans BigQuery for contracts carrying deferred balances past their expected recognition date and writes a triaged exception list to Snowflake plus a Slack digest.

CategoryFinance
Enginesim
Difficultyintermediate
Triggerschedule
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly audit schedule
  • ActionFind aged open deferred balances in BigQueryGoogle BigQueryBigQuery
  • LogicRank exceptions by amount and aging bucket
  • ActionPersist exception list to SnowflakeSnowflakeSnowflake
  • OutputSend top exceptions to SlackSlack

What it does

Deferred revenue that should have recognized but didn't is a classic close error — usually a schedule that stalled while billing kept going. This workflow finds those: it queries BigQuery for contracts whose deferred balance is still open past the period it should have cleared, buckets them by how overdue they are, and reports the exceptions. The outcome is recovered, correctly-timed revenue and a cleaner balance sheet.

When to use it

Run it weekly when you suspect recognition schedules silently stop firing, or when auditors keep finding aged deferred balances you didn't know about. It's a detective control that runs between closes.

How it works

  1. 1A weekly schedule starts the audit.
  2. 2A BigQuery query finds contracts with open deferred balances past their last expected recognition period and tags each with an aging bucket.
  3. 3A logic step ranks exceptions by overdue amount and age so the worst surface first.
  4. 4A Snowflake action persists the full exception list for the audit trail and BI dashboards.
  5. 5A Slack digest sends the top exceptions to the revenue team for clearing.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect BigQueryDatasets, queries, schemas.
  2. 2
    Connect SnowflakeWarehouses, queries, shares.
  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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