FINANCE

Weekly Reimbursement Audit to Monday Review Board

Each week, pulls the prior week's submitted reimbursements, flags the ones that exceed policy thresholds or look statistically unusual.

CategoryFinance
Enginesim
Difficultyintermediate
Triggerschedule
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly schedule fires Monday morning
  • ActionQuery last 7 days of reimbursementsPostgreSQLPostgres
  • LogicFlag over-cap and above-baseline charges
  • ActionSummarize each outlier with suggested actionOpenAI
  • OutputCreate review cards on Monday boardmonday.com

What it does

On a fixed weekly schedule, this workflow gathers all reimbursement requests submitted in the last seven days, evaluates each against fixed policy caps and against the employee's own historical baseline, and creates a tracked review item on a Monday board for every charge that stands out. Each item carries the amount, the reason it was flagged, and the submitter.

When to use it

Use this when reimbursements are batch-reviewed rather than caught live, and you want a defensible weekly audit trail with one owner per flagged item. Good for teams that run approvals through Monday.

How it works

  1. 1A weekly schedule trigger starts the run every Monday morning.
  2. 2A Postgres step queries the last week of reimbursement rows from your finance database.
  3. 3A logic step flags rows over hard caps or more than a set multiple above the submitter's trailing average.
  4. 4An OpenAI step groups and summarizes each flagged item with a suggested action.
  5. 5A Monday action creates one review card per outlier, assigned to that employee's manager with a due date.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect PostgresAny Postgres URL — query, write, migrate.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect monday.comVisual work management for teams.
  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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