Analyze·Advanced·60 min·Updated Sep 30, 2026

Flag suspicious vendor activity with a spend anomaly agent

Build a Copilot Studio agent that watches procurement data for fraudulent or unusual vendor patterns.

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Good fit for

Finance

Microsoft 365

Excel

Works With

Copilot Studio

Business Outcome

Suspicious vendor activity gets surfaced for review before it becomes a real loss, not discovered in a year-end audit.

Handle with care

Fraud-detection methodology

The specific thresholds and patterns this agent watches for are themselves sensitive — if someone trying to commit fraud learns the exact rules, they can structure transactions to slip under them. Keep the rule list restricted to the finance/audit team that maintains it.

Workflow Overview

Excel

Copilot Studio

Step 1: Define what "suspicious" means for your businessExcel

Write each pattern as a specific, checkable rule with real numbers — "paid within 24 hours of invoice receipt," "round amount over $5,000," "monthly spend more than 3x the trailing 6-month average".

Rather than a vague instinct like "anything that looks off." A rule the agent can evaluate consistently is what keeps the flags defensible instead of arbitrary.

Step 2: Build the agent against your procurement exportCopilot Studio

Point a Copilot Studio agent at the procurement/AP data and give it the specific patterns to watch for instead of an open-ended "find fraud" instruction.

Prompt idea:

Scan this procurement export for: duplicate invoice numbers, invoices paid within 24 hours of receipt, round-number invoices over $5,000, and any vendor whose monthly spend is more than 3x their trailing 6-month average. List each flagged transaction with the specific rule it triggered.

Step 3: Route flags to the right reviewerExcel

Send the flagged list to procurement or internal audit rather than acting on it automatically — false positives are common and the agent doesn't know your vendor relationships.

Step 4: Tune the thresholds over timeExcel

Track how many flags turn out to be real issues vs. false positives, and adjust the rules so the agent stays useful instead of getting ignored.

Check the work

Confirm each flagged transaction actually matches the rule cited, not just something that looks similar.
Check false-positive rate before treating any flag as confirmed fraud.
Review whether the flagged patterns are the right ones for your actual procurement risk, not a generic list.

Source: Microsoft Copilot Scenario Library — Finance (2026)

Expected Outcome

A watchlist of flagged vendor transactions with the specific anomaly pattern that triggered each flag.

Is AI actually needed here?

Every pattern listed — a duplicate invoice number, a round amount, payment within 24 hours, spend 3x the trailing average — is an explicit, checkable rule against data already in the export.

Non-AI alternative: Power Query or Excel formulas (a duplicate check, a round-number test, a date difference, a ratio to trailing average) already flag the same transactions deterministically, and many AP systems do this natively.