INVOICE PROCESSING

Month-end FX revaluation of open invoices into a Snowflake report

On the last business day it revalues every open foreign invoice at the month-end closing rate, builds an unrealized FX gain/loss snapshot in Snowflake.

CategoryInvoice Processing
Enginesim
Difficultyadvanced
Triggerschedule
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerLast-business-day-of-month schedule
  • ActionPull open foreign invoices from PostgresPostgreSQLPostgres
  • ActionFetch month-end closing rate per currencyHTTP webhook
  • LogicCompute unrealized gain/loss and totals
  • ActionWrite revaluation snapshot to SnowflakeSnowflakeSnowflake
  • OutputNotify close team in Microsoft TeamsMicrosoft Teams

What it does

This workflow produces the month-end FX revaluation that accounting needs for close. It takes every open foreign-currency invoice, revalues it at the official month-end closing rate, computes the unrealized gain or loss versus the posting-date rate, and lands a dated snapshot in Snowflake.

When to use it

Use it at period close when you must restate open foreign balances to the closing rate and document unrealized FX adjustments. It gives controllers a repeatable, queryable snapshot instead of a manual spreadsheet revaluation each month.

How it works

  1. 1A schedule fires on the last business day of the month.
  2. 2A Postgres query pulls all open foreign-currency invoices with their posting-date rates.
  3. 3An HTTP action fetches the month-end closing rate per currency.
  4. 4A logic step computes unrealized gain or loss for each invoice and totals it by currency.
  5. 5A Snowflake action writes the dated revaluation snapshot.
  6. 6A Microsoft Teams output notifies the close team that the revaluation is ready.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect PostgresAny Postgres URL — query, write, migrate.
  2. 2
    Connect HTTP webhookTrigger any URL on agent actions.
  3. 3
    Connect SnowflakeWarehouses, queries, shares.
  4. 4
    Connect Microsoft TeamsChannels, chats, files.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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