Procurement Fraud Detection System for ₹700+ Cr in Annual Spend
Impact Statement
Embedded automated checks across ₹700+ Cr in manufacturing procurement to detect mismatches and prevent fraud.
Scenario
A heavy metals manufacturing company was making over ₹700 Cr worth of purchases annually. Management wanted to strengthen procurement controls to prevent fraud, overbilling, and process inefficiencies before they caused significant losses.
Mhymatch was engaged to design and implement a system that continuously monitors procurement transactions for anomalies.
Data Ingested
Purchase orders (POs) and approval records
Goods Receipt Notes (GRNs) from warehouse and production sites
Supplier invoices and delivery notes
Lorry receipts and transport logs
Defect tags and quality inspection reports
MIGO (101) transaction logs from SAP
Supplier master data and contract terms
Process (The Prevention Loop in Action)
Ingest: Pulled procurement data from SAP, supplier records, and logistics systems into a unified repository.
Normalize: Standardized item codes, supplier IDs, and document formats for automated matching.
Link: Connected POs, GRNs, invoices, and transport logs to form a complete transaction chain.
Analyze:
Automated tests for GRN–invoice–PO alignment on quantity, date, and supplier.
Signature & presence verification on delivery notes.
Defect-to-block rules: flagged cases where defective goods were still invoiced and paid.
Timeliness thresholds: identified delays between goods receipt and invoice processing.
Act:Created an exception report highlighting mismatches and anomalies (~8% of total sample value).
Built a CFO-ready dashboard to track anomalies and leakage risk in real time.
Delivered control refinement recommendations for procurement and warehouse teams.
Learn: Embedded GRN–invoice–PO reconciliation into Controls-as-Code for ongoing monitoring of all ₹700+ Cr spend.
Output
System capable of real-time mismatch detection across all procurement transactions.
Exception list with detailed mismatch categories and supporting evidence.
Value-at-risk quantification for anomalies found in sample data.
Impact
Continuous monitoring of ₹700+ Cr procurement spend.
Stronger controls to prevent overbilling, duplicate payments, and fraud.
Reduced payment cycle time and improved supplier accountability.
Call to Action
Want to make procurement fraud almost impossible?
Run our 72-Hour Baseline Map to design your own real-time anomaly detection system.