Back to SAP S/4HANA Plugin Platform
AI-Powered Fraud Detection for SAP S/4HANA

Six Fraud Engines.
One Unified Shield.
420ms to Block a $49,500 Fraud.

Real-time fraud detection that runs inside your SAP S/4HANA — analyzing every transaction across 6 parallel AI engines before it posts. No GPU. No training. No data leaves your system.

99.1%
Detection Accuracy
<500ms
Analysis Time
50K+
Fraud Signatures
6
Parallel Engines
SOX Compliant
GDPR Ready
98%+ Accuracy
Zero False Negatives
Six Engines, One Shield

The 6 Fraud Detection Engines

Each engine specializes in a different fraud vector. All six run in parallel on every transaction, completing their analysis in under 500 milliseconds — on CPU alone.

ENGINE 1

Duplicate Payment Detection

99.1% Accuracy

Detects duplicate, near-duplicate, and intentionally-disguised invoices across millions of transactions. Catches character substitution, transposition, and OCR-induced duplicates that rule-based systems miss.

Algorithms
Levenshtein DistanceDBSCAN ClusteringTF-IDF Vectorization
Real Detection Example

Invoice INV-2026-1234 from "Acme Industrial" for $47,200 flagged as near-duplicate of INV-2026-I234 (letter I swapped for digit 1) from "Acme lndustrial" (lowercase L for uppercase I). Same amount, same date, different bank account. Levenshtein distance: 2. TF-IDF cosine similarity: 0.97. Verdict: BLOCK.

ENGINE 2

Contract Leakage Detection

98.0% Accuracy

Compares every invoice line item against contracted prices, volumes, and terms. Catches pricing overcharges, volume discount failures, and contract term violations that silently erode margins.

Algorithms
IsolationForestPrice Variance AnalysisContract Term Matching
Real Detection Example

Vendor "GlobalParts Inc." invoiced $12.50/unit for Part #GP-4422. Contract specifies $10.00/unit for orders over 500 units (this order: 2,400 units). Leakage: 25% overcharge = $6,000 on this single PO. Annualized leakage from this vendor alone: $72,000. Verdict: FLAG + auto-generate credit memo request.

ENGINE 3

Pricing & Discount Fraud

99.0% Accuracy

Monitors discount patterns, price overrides, and manual adjustments in real time. Detects unauthorized discounts, after-hours changes, and statistically impossible pricing patterns using Benford's Law digit analysis.

Algorithms
Z-Score AnalysisBenford's LawTime-Series Anomaly Detection
Real Detection Example

Sales rep changed discount from 2% to 18% on a $340,000 order at 11:45 PM Saturday. Benford's Law analysis: first-digit distribution of this rep's discounts deviates 4.2 standard deviations from expected. Historical average discount for this customer tier: 3.1%. Z-Score: 4.8. Verdict: BLOCK + escalate to Sales VP.

ENGINE 4

Three-Way Match Automation

99.1% Accuracy

Automatically matches Purchase Orders, Goods Receipts, and Invoices with intelligent fuzzy matching. Catches quantity discrepancies, unit price variances, and partial delivery fraud that manual matching misses.

Algorithms
Fuzzy MatchingRule EngineTolerance Band Analysis
Real Detection Example

PO #4500012345: 1,000 units at $25.00. GR #5000067890: 980 units received (2% shortage). Invoice #VND-2026-8901: 1,000 units at $25.50. Two anomalies caught: (1) Invoice quantity exceeds received quantity by 20 units = $510 overcharge. (2) Unit price $0.50 above PO price = $500 overcharge. Total prevented: $1,010. Verdict: PARTIAL BLOCK.

ENGINE 5

Transfer Pricing Optimization

98.0% Accuracy

Validates intercompany transfer prices against OECD arm's length principles in real time. Detects non-compliant pricing that creates tax audit risk and potential penalties before transactions post.

Algorithms
Regression AnalysisOECD BenchmarkingComparable Uncontrolled Price Method
Real Detection Example

Intercompany invoice: German subsidiary charging Irish entity EUR 450/unit for component #IC-7700. OECD CUP benchmark range: EUR 280-360. Market comparable: EUR 320. Deviation: +40.6% above market. Tax risk: potential EUR 2.1M adjustment across annual volume. OECD non-compliance flag. Verdict: FLAG + generate transfer pricing documentation.

ENGINE 6

Tare Weight Fraud Detection

97.0% Accuracy

Monitors vehicle and container tare weights at receiving docks. Detects inflated tare weights used to under-report delivered quantities — a common fraud in bulk commodities, chemicals, and agriculture.

Algorithms
Zynoviq Gradient BoosterAnomaly Detection EngineStatistical Process Control
Real Detection Example

Delivery truck #TRK-4422 reports tare weight of 9,500 kg. Historical average for this vehicle: 7,800 kg. Anomaly detection score: 0.89 (threshold: 0.65). Gradient Booster confirms: 94% probability of inflated tare. Net weight discrepancy: 1,700 kg of raw material = $8,500 in undelivered goods. Verdict: BLOCK + require re-weigh.

Live Detection Walkthrough

The $49,500 Invoice:
9 Checks. 420 Milliseconds. Blocked.

An AP Clerk posts a $49,500 invoice — exactly $500 below the $50,000 approval limit. Here is exactly what happens inside FraudGuard in the next 420 milliseconds.

FRAUDGUARD REAL-TIME ANALYSIS
Invoice
INV-2026-49500
Amount
$49,500.00
Vendor
Acme lndustrial LLC
Submitted
Sat 11:52 PM
9 Parallel Fraud Checks — Executed in 420ms
Threshold Gaming DetectionSUSPICIOUSConfidence: 89%

$49,500 is exactly $500 below the $50,000 approval threshold

Round Number AnalysisSUSPICIOUSConfidence: 72%

$49,500 ends in multiple zeros — Benford's Law violation on first two digits

Bank Account ChangeHIGH RISKConfidence: 95%

Vendor bank account changed 3 days before invoice submission

Social Network / CollusionHIGH RISKConfidence: 91%

Invoice approver shares home address with vendor contact (public records match)

Vendor TyposquattingCRITICALConfidence: 97%

"Acme lndustrial" (lowercase L) vs legitimate "Acme Industrial" (uppercase I) — Levenshtein: 1

Time Anomaly DetectionSUSPICIOUSConfidence: 78%

Invoice submitted at 11:52 PM Saturday — outside normal business hours

PO ValidationFAILEDConfidence: 99%

Referenced PO #4500099887 has remaining balance of only $12,300 — invoice exceeds by $37,200

Historical Pattern AnalysisANOMALYConfidence: 88%

This vendor has never invoiced above $15,000 in 3 years of transaction history

Behavioral BaselineSUSPICIOUSConfidence: 82%

AP Clerk has processed 340% more invoices this week than 52-week average

Final Ensemble Score
Weighted Score: 94 / 100 — Threshold: 80

9 checks completed in 420ms. 7 of 9 flagged anomalies. Cross-correlation boost: +12 points. Decision: BLOCK. Transaction prevented from posting to SAP.

94
BLOCKED
The Cost of Inaction

The Cost of Missing Fraud

Most organizations discover fraud 18 months too late. By then, the damage is done — and the money is gone.

$4.7T
Annual Global Fraud
Association of Certified Fraud Examiners (ACFE) 2024 Report
5%
Revenue Lost to Fraud
Average across all industries — often invisible to leadership
18 mo
Average Detection Time
Median time from fraud inception to discovery
$150K
Average Scheme Size
Median loss per fraud case before detection

For a $1B Revenue Company

At the industry average of 5% revenue loss, that is $50 million per year in fraud exposure. Even catching 1% of that with FraudGuard pays for the platform 37,000 times over.

FraudGuard: Contact Sales for Pricing
Typical Year 1 recovery: $1.2M - $4.8M
ROI: 89,000% - 357,000%
Your Annual Fraud Exposure
5%
of total revenue
Industry average — ACFE 2024
Real-Time Detection Flow

From Transaction to Decision
in Under 500 Milliseconds

Every transaction flows through this pipeline. No exceptions. No batch processing. No overnight reports. Real-time, pre-commit fraud prevention.

01

Transaction Posted

AP Clerk posts invoice in SAP S/4HANA

02

SAP Event Mesh

Real-time event triggers FraudGuard via SAP BTP Event Mesh

03

FraudGuard Receives

Transaction payload received and parsed in <10ms

04

6 Engines in Parallel

All 6 fraud engines analyze simultaneously on CPU

05

Score Aggregation

Weighted ensemble score computed across all engines

06

BLOCK or ALLOW

Score > 80 = BLOCK. Score 60-80 = REVIEW. Score < 60 = ALLOW

07

Audit Trail

SHA-256 hash-chained, tamper-proof log with full reasoning

08

Alert to CFO

Real-time notification via SAP Fiori, email, and Microsoft Teams

Total Pipeline Latency
420ms average | P99: 480ms | P99.9: 498ms
Competitive Comparison

FraudGuard vs. SAP GRC vs. SAS

SAP GRC Process Control is rule-based and post-commit. SAS requires months of training and GPU clusters. FraudGuard is AI-powered, pre-commit, and deploys in 12 minutes.

Feature
Zynoviq FraudGuard
SAP GRC Process ControlSAS Fraud Detection
Detection TimingPre-commit (blocks before posting)Post-commit (detects after payment)Batch (detects hours/days later)
AI-Powered Detection6 ML engines + LLM reasoningRule-based onlyStatistical models (requires training)
Deployment Time12 minutes (one MTA file)6-18 months12-24 months
Cross-Module AnalysisFI, MM, SD, HR, TR — unifiedFI/CO focusedRequires separate integration
Detection Accuracy99.1% (Day 1, zero training)60-75% (rule dependent)85-92% (after 6mo training)
Total Cost (Year 1)Contact Sales$150K-500K + consultants$500K-2M + infrastructure
GPU RequirementNone — CPU onlyN/A (no AI)GPU cluster required
Data Sovereignty100% — never leaves SAP BTPOn-premise or SAP cloudData sent to SAS cloud

The Key Difference: Pre-Commit vs. Post-Commit

SAP GRC and SAS detect fraud after the payment has been made — you are chasing money that is already gone. FraudGuard blocks fraudulent transactions before they post to SAP. The money never leaves your account. That is not an incremental improvement — that is a fundamentally different approach to fraud prevention.

CFO Impact

What FraudGuard Means for Your CFO

FraudGuard is not a security tool — it is a financial performance tool. Every blocked fraud is recovered margin. Every automated audit is time back for your finance team.

Direct P&L Protection

$2.3M saved in first 90 days

FraudGuard catches duplicate payments, contract leakage, and pricing fraud that directly flows to your bottom line. Every blocked fraudulent payment is pure margin recovery.

$1.2M-4.8M
Avg. recovery (Year 1)
<30 days
ROI payback

Audit Readiness

From 6-week audit prep to 1 click

Every transaction decision is logged with SHA-256 hash-chained audit trails. Full reasoning, confidence scores, and engine verdicts — exportable in seconds for any auditor.

-95%
Audit prep time
100% automated
Documentation

SOX Compliance

Sections 302, 404, 409 — automated

Continuous monitoring of all financial controls. Segregation of duties enforcement, approval threshold compliance, and real-time material weakness detection — all automated.

100%
SOX controls monitored
Real-time alerts
Compliance gaps

Board Confidence

Certify with confidence every quarter

When you sign the SOX 302 certification, you know every transaction has been analyzed by 6 AI engines. No more "hope nothing slipped through" — you have mathematical proof.

100%
Transaction coverage
99.1%
Certification confidence
Ready to Protect Your Revenue

Stop Losing Money
You Don't Know You're Losing

90-day free trial. All 6 fraud engines included. No credit card. No commitment. Your data never leaves your system. Deploy in 12 minutes.

Free Trial
6 Fraud Engines
99.1% Accuracy
<500ms Detection
Zero Data Export