FINANCE

Vendor Spend Spike Detector

Watches month-to-date spend by vendor in Snowflake during close, flags vendors whose spend jumped beyond a percentage and absolute threshold versus their typical month.

CategoryFinance
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerDaily schedule through close window
  • ActionAggregate MTD spend per vendor in SnowflakeSnowflakeSnowflake
  • LogicFlag vendors past percentage and dollar thresholds
  • ActionPull contributing invoices per flagged vendorSnowflakeSnowflake
  • LogicScreen out known seasonal or contractual patterns
  • OutputAlert AP lead in Microsoft Teams with invoicesMicrosoft Teams

What it does

This workflow isolates anomalies at the vendor level rather than the GL-account level. It aggregates month-to-date payables by vendor in Snowflake, compares each vendor against its trailing-average monthly spend, and flags any that spiked past both a percentage and a dollar threshold. For each flagged vendor it lists the specific invoices driving the spike and alerts the AP lead in Teams.

When to use it

Use it during close to catch duplicate payments, mis-coded invoices, or genuine spend surprises before they get buried in the GL. Built for AP and procurement-finance leads who own vendor cost accuracy.

How it works

  1. 1A schedule fires daily through the close window.
  2. 2A Snowflake query aggregates MTD spend per vendor with the trailing monthly average.
  3. 3A logic step flags vendors breaching both the percentage and dollar thresholds.
  4. 4A second Snowflake query pulls the contributing invoices for each flagged vendor.
  5. 5A logic step screens out vendors with known seasonal or contractual spend patterns.
  6. 6An alert with vendors and offending invoices is sent to the AP lead in Microsoft Teams.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SnowflakeWarehouses, queries, shares.
  2. 2
    Connect Microsoft TeamsChannels, chats, files.
  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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