15,000 Invoices Per Month.
Your Team Reviews 300.
The Fraud Lives in the Other 14,700.
FraudGuard deploys 6 AI-powered detection engines inside your SAP S/4HANA to analyze every invoice, every goods receipt, every vendor payment in under 500 milliseconds. No sampling. No annual audits. No separate tool. Fraud is blocked before the transaction posts to your ledger.
The 5 Procurement Fraud Schemes Hiding in Your SAP
These are not theoretical risks. These are active fraud patterns our engines detect in production SAP environments every week. The dollar figures represent annualized losses from real customer deployments.
Shell Company Networks
Fake vendors created to siphon payments. Same bank account routed through different company names. Slight name variations — "Apex Industrial LLC" and "Apex lndustrial LLC" (lowercase L for uppercase I). AP teams see two separate vendors. The bank sees one account receiving double payments.
Levenshtein distance analysis scores vendor name similarity. Network graph analysis maps shared bank accounts, addresses, phone numbers, and tax IDs across your entire vendor master. When two "unrelated" vendors share a bank account, FraudGuard flags it in <200ms — before the payment posts.
Invoice Splitting
Invoices land at $49,500 — just under the $50,000 approval threshold that triggers senior review. Same vendor, dates shifted 3-5 days apart, PO numbers transposed by a single digit. Each invoice looks routine. Together, they represent a systematic scheme to bypass your approval controls.
DBSCAN clustering identifies temporal and amount patterns invisible to threshold-based rules. FraudGuard correlates invoice amounts, submission dates, vendor IDs, and PO references to detect clustering just below any approval limit — even limits that differ across company codes.
Contract Price Drift
Vendors overcharging 2-4% above contracted rates on 200+ active contracts. Each line item is small enough to pass tolerance checks. Across thousands of invoices per year, the cumulative overcharge reaches millions. Annual contract reviews catch it 12-18 months too late.
Real-time contract-vs-invoice comparison on every single transaction. FraudGuard pulls the contracted unit price from the SAP purchasing info record and compares it against the invoiced unit price at line-item level. Any deviation above the configured tolerance triggers an immediate hold.
Three-Way Match Bypass
The purchase order says 1,000 units at $10.00. Goods receipt confirms 980 units received. The invoice arrives for 1,000 units at $10.30. The quantity discrepancy is within tolerance. The price discrepancy is within tolerance. Together, the overpayment is $10,294 — and the system approves it automatically.
Fuzzy matching + tolerance band analysis + 240 validation rules catch what threshold-based systems miss. FraudGuard evaluates PO, GR, and invoice as a unified transaction — not three independent documents. Combined deviations that individually pass but collectively fail are blocked.
Tare Weight Manipulation
Trucks arriving at your receiving dock with tare weights 1,700kg heavier than historical average for that vehicle. Each incident means $8,500 in under-delivered raw materials billed at full weight. With 480+ deliveries per month, even a 5% manipulation rate costs millions annually.
CUSUM statistical process control tracks tare weight drift over time. IsolationForest anomaly detection identifies statistical outliers without labeled training data. Gradient boosting builds predictive models per vehicle. All three algorithms fire in parallel in <200ms at the weigh bridge.
These figures represent documented losses from audited customer deployments. Undetected fraud — schemes not yet identified — adds an estimated 30-40% to the total.
The Procurement Transformation: Before & After
Six dimensions of procurement fraud prevention — transformed from manual, reactive processes to AI-powered, real-time protection.
Real-Time Vendor Intelligence Dashboard
No separate tool. No new login. No browser tab to forget. FraudGuard lives inside your existing SAP Fiori launchpad — the same interface your procurement team uses every day.
Vendor Risk Heatmap
Every vendor in your master data color-coded by composite fraud risk score. Green (0-30), amber (31-70), red (71-100). Drill down to see which engines triggered, which invoices flagged, and historical trend.
Invoice Pattern Analysis
Real-time visualization of invoice amount distributions per vendor. Histogram overlays show clustering around approval thresholds. Temporal scatter plots reveal date-shifting patterns across related vendors.
Cross-Vendor Correlation
Network diagram showing shared bank accounts, addresses, phone numbers, and contact persons across your entire vendor master. Connected nodes indicate potential shell company relationships requiring investigation.
Threshold Gaming Alerts
Dedicated alert feed for invoices clustering within 5% below any approval limit. Shows vendor name, amount, threshold proximity percentage, and historical frequency of near-threshold submissions.
Contract Compliance Meter
Per-vendor, per-contract adherence rate displayed as a gauge. Shows cumulative overcharge amount, number of deviating line items, and trend direction. Red contracts flagged for immediate procurement review.
Live dashboard feed from production deployment. All vendor names and amounts anonymized.
$420M Manufacturer — $2.4M Invoice Splitting Scheme Caught in Week 1
The Company
$420M annual revenue industrial manufacturer. 12 SAP company codes across 4 countries. 15,000 invoices per month flowing through accounts payable. Standard SAP approval workflows with $50K senior review threshold. Internal audit team of 6.
The Scheme
Three vendor accounts — registered in different company codes — submitted invoices consistently at $49,500. Every invoice fell just below the $50,000 threshold that required senior management approval. Submission dates were shifted 3-5 days apart. PO reference numbers were transposed by a single digit (PO-78234 vs PO-78243). Each vendor appeared to be an independent, low-risk supplier. The scheme had been running for 18 months. Internal auditors reviewed the accounts twice during that period and found nothing — because each vendor, viewed in isolation, showed no anomalies.
The Detection
FraudGuard was deployed on a Monday morning. By Monday afternoon, the DBSCAN clustering engine had identified cross-vendor pattern correlation: three vendors, identical invoice amounts, dates within a 5-day window, PO numbers with Levenshtein distance of 1. The network graph engine confirmed all three vendors shared a registered agent address. Total past fraud: $2.4M over 18 months. Ongoing contract leakage discovered during investigation: $180K per month in overbilled line items across the same vendor network.
The Outcome
Total recovered: $2.4M in historical fraudulent payments. $180K/month in ongoing contract leakage stopped immediately. 94 compliance gaps identified across approval workflows, vendor onboarding, and three-way match tolerances. The three vendor accounts were frozen within 48 hours. Criminal referral filed within 2 weeks.
Detection Timeline
We found $340K in duplicates the auditors missed for two years. FraudGuard detected patterns that no human reviewer — no matter how experienced — could see across 15,000 invoices per month.
Operations Floor Impact: Tare Weight + Three-Way Match
Two engines purpose-built for the operations floor. These are not back-office analytics tools — they intercept fraud at the point of transaction: the weigh bridge and the goods receipt desk.
Tare Weight Fraud Engine
Trucks arriving at your receiving dock with declared tare weights 1,700kg above historical baseline for that specific vehicle. Each incident means $8,500 in under-delivered raw materials billed at full delivery weight. With hundreds of deliveries per month, a systematic 5% manipulation rate generates millions in annual material losses that appear as normal inventory variance.
CUSUM statistical process control tracks tare weight drift over time — detecting gradual inflation that individual checks miss. IsolationForest anomaly detection flags statistical outliers without requiring labeled training data. Gradient boosting builds a predictive model per vehicle, learning seasonal variations (winter tires, fuel loads) and distinguishing legitimate changes from manipulation. All three algorithms fire in parallel in under 200 milliseconds at the weigh bridge.
Three-Way Match Engine
340 tolerance overrides approved per month without manual review. Standard SAP tolerance checks evaluate PO-vs-invoice and GR-vs-invoice independently. When each individual comparison passes within tolerance, the combined overpayment — quantity variance plus price variance — can range from $1,000 to $50,000 per invoice. At 340 overrides per month, the annual exposure exceeds $3.7M.
240 validation rules evaluate the purchase order, goods receipt, and invoice as a unified transaction — not three separate documents. Fuzzy matching catches transposed quantities, unit-of-measure mismatches, and partial delivery billing errors. Combined tolerance analysis flags invoices where individual deviations pass but the aggregate exceeds acceptable limits. Every override is scored, logged, and routed for appropriate review.
We had 340 tolerance overrides per month going through without review. FraudGuard flagged 47 of those as genuine overpayments in the first week — $184,000 that would have posted to the ledger. The tare weight engine caught a manipulation pattern at our receiving dock that had been running for 14 months.
Every Invoice You Don't Review Is an Invitation to Fraud
Your AP team cannot review 15,000 invoices per month. FraudGuard can — in under 500 milliseconds each. Deploy 6 AI detection engines inside your SAP S/4HANA in 12 minutes. No data extraction. No separate tool. No training required.