Risk and assurance

Procurement Fraud Signal Detection

Name: Procurement Anomaly Detection
Brand: MII Microsoft

In trial

Detects fraud signals across procurement documents by extracting fields and comparing transaction patterns.

Business value

Stronger procurement oversight, earlier risk detection, less manual document review, and clearer evidence packs for audit or investigation teams.

Procurement reviews often depend on manual document checks across invoices, purchase orders, vendor records, and supporting files, where anomalies are easy to miss and evidence is time-consuming to gather. Procurement Anomaly Detection is an MII-built capability pattern that reads purchase documents, extracts vendors, values, dates, items, and supporting evidence, compares the details against expected patterns, and highlights anomalies or fraud signals with context for reviewer validation. Risky transactions are surfaced earlier with extracted evidence and fraud indicators organized for human reviewer validation, which strengthens procurement controls and shortens audit and investigation work.

Where it helps

Identifying split purchases, inflated prices, and duplicate invoices automatically
Flagging vendor anomalies and unusual transaction patterns
Assembling evidence packs for audit and investigation teams

What the pattern does

Automated procurement field extraction from documents
Anomaly and fraud indicator detection
Evidence-led review with organized supporting documentation

The challenge

Procurement reviews often depend on manual document checks across invoices, purchase orders, vendor records, and supporting files. Anomalies are easy to miss, evidence is time-consuming to gather, and controls become difficult to enforce consistently at scale.

What was built

The solution reads purchase documents, extracts vendors, values, dates, items, and supporting evidence, compares the details against expected patterns, and highlights anomalies or fraud signals with context for reviewer validation.

What changed

Risky transactions are surfaced earlier with extracted evidence, anomaly context, and fraud indicators organized for human reviewer validation.

Why it matters