Available with: Copilot Studio
Finance scenario: Spend anomaly identification agent
Scenario
Using AI Agents to build a spend anomaly identification agent
Automate detection of suspicious vendor activity, abnormal spending patterns, and duplicate invoices, using AI-powered anomaly detection and fraud detection logic.
Extracts financial transactions from ERP systems and monitors transactions continuously.

Copilot Studio Agent
Continuously pulls transaction data to enable proactive anomaly detection.
Transforms data into a clean, consistent and analysis ready format so that it’s ready for anomaly detection.

Copilot Studio Agent
Cleans and formats data for consistent, accurate analysis.
Applies rule-based and pattern-based logic and detects patterns in vendor behavior and payment history.

Copilot Studio Agent
Identifies suspicious trends in vendor behavior and payment activity.
Scores each transaction based on predefined risk metrics and flags high-risk transactions for review.

Copilot Studio Agent
Flags high-risk transactions based on predefined risk rules and metrics.
Flags transactions with internal policies and regulatory requirements for compliance validation cross-references.

Copilot Studio Agent
Ensures every transaction aligns with internal policies and regulatory standards.
Provides actionable insights for resolving flagged issues & automates follow-up actions.

Copilot Studio Agent
Provides clear next steps to resolve flagged anomalies and prevent future issues.
The content in this example scenario is for demonstration purposes only. You should evaluate how Copilot aligns with your organization’s business processes, regulatory requirements, and responsible AI principles.
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