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Real-World Impact

16 Real-World Scenarios.
Real Results. Real ROI.

See exactly how enterprises deploy FraudGuard, Compliance Autopilot, SupplyChain Prophet, and VoiceOps AI to protect revenue, ensure compliance, and accelerate operations.

16
Use Cases
6
Industries
4
AI Engines
$50M+
Protected
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Explore All 16 Use Cases

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Showing 16 of 16 use cases
FraudGuard
Manufacturing

$49,500 Invoice — Just Below Approval Threshold

The Problem

A vendor submitted an invoice for $49,500 — exactly $500 below the $50,000 approval limit. The vendor name was off by one letter from a legitimate supplier, and the bank account had been changed just 3 days earlier.

How It Was Caught

FraudGuard correlated three independent signals: threshold gaming pattern (amount within 1% of limit), fuzzy name matching flagged a Levenshtein distance of 1 from known vendor, and bank change recency analysis detected the 3-day window. Combined risk score: 94.

Impact
$49.5K saved
Detection
420ms
FraudGuard
Banking

Ghost Vendor Network — 3 Shell Companies

The Problem

Three seemingly unrelated vendors were submitting invoices for IT consulting services. Different names, different addresses, different tax IDs. No single invoice exceeded $25,000.

How It Was Caught

Network analysis engine detected all three vendors submitting from the same IP address, with invoices created within minutes of each other. Graph-based collusion detection identified the shell company ring through shared metadata patterns.

Impact
$1.2M prevented
Detection
1.2s
FraudGuard
Manufacturing

Contract Price Leakage — 12% Drift Over 18 Months

The Problem

Procurement was consistently paying $47/unit when the negotiated contract price was $42/unit. The 12% price drift accumulated silently over 18 months across 14,000 purchase orders.

How It Was Caught

Contract compliance engine compared every PO line item against master agreement pricing. Statistical drift analysis identified the systematic overcharge pattern that started as $0.50 and grew to $5.00 per unit over time.

Impact
$340K recovered
Detection
890ms
FraudGuard
Automotive

Three-Way Match Fraud — 200-Unit Phantom Delivery

The Problem

Purchase order specified 1,000 units. Goods receipt confirmed only 800 units received at the warehouse. But the vendor invoice claimed 1,000 units delivered. The 200-unit gap represented phantom deliveries being billed.

How It Was Caught

Three-way match engine detected the PO-GR-Invoice quantity mismatch. Cross-referenced historical GR data to confirm the pattern — this vendor had a consistent 15-20% overbilling pattern across 8 months of transactions.

Impact
$89K prevented
Detection
650ms
FraudGuard
Oil & Gas

Transfer Pricing Manipulation — 40% Intercompany Markup

The Problem

An intercompany invoice between subsidiaries showed a 40% markup on technical services. Internal transfer pricing policy and OECD arm's length principle both indicated the acceptable range was 12-15%.

How It Was Caught

Transfer pricing engine compared the markup against OECD benchmarks, internal policy thresholds, and historical intercompany transaction patterns. The 40% markup was 2.8x the acceptable range, triggering automatic escalation.

Impact
$2.1M risk averted
Detection
780ms
FraudGuard
Manufacturing

Tare Weight Fraud — Systematic 3-5% Manipulation

The Problem

Bulk raw material deliveries were consistently showing weights 3-5% below expected values. Individual variances were within "acceptable tolerance," making the pattern invisible to manual review.

How It Was Caught

Statistical anomaly engine applied Benford's Law analysis and Z-score deviation tracking across 2,400 delivery records. Detected that weight variances were not randomly distributed — they were systematically biased toward the supplier's favor.

Impact
$180K/yr recovered
Detection
1.1s
Compliance Autopilot
Pharma

$12.5M Intercompany Invoice — Triple Compliance Violation

The Problem

A $12.5M intercompany invoice from the Ireland subsidiary to the US headquarters required validation against SOX segregation of duties, OECD transfer pricing guidelines, and FinCEN currency transaction reporting thresholds.

How It Was Caught

Compliance Autopilot ran 15 parallel regulatory checks in under 500ms. Caught a SOX SoD violation (same person approved PO and invoice), flagged OECD transfer pricing non-compliance (14.7% vs 3-7% benchmark), and auto-generated a FinCEN CTR filing for the amount exceeding $10K threshold.

Impact
$12.5M protected
Detection
420ms
Compliance Autopilot
Banking

GDPR Right to Erasure — Full SAP Module Sweep

The Problem

A customer exercised their GDPR Article 17 right to erasure. Their personal data existed across 14 SAP modules — FI, SD, CRM, HR, MM, and more. Manual identification and deletion would take weeks and risk incomplete coverage.

How It Was Caught

Data lineage engine traced the customer's PII across all SAP modules, identified 847 data records in 14 modules, generated a deletion plan with dependency ordering, and executed the erasure with a complete audit trail — all within the GDPR 72-hour compliance window.

Impact
GDPR fine averted
Detection
< 2 hours
Compliance Autopilot
Banking

SOX Segregation of Duties — Same Approver Flagged

The Problem

The same individual approved both the purchase order creation and the corresponding invoice payment. This represents a clear SOX Section 404 segregation of duties violation that could trigger audit findings.

How It Was Caught

SoD engine cross-referenced user roles across the procure-to-pay cycle. Detected that User ID 4782 had both PO approval and invoice approval authority, and had exercised both on the same transaction. Automatically routed to an alternate authority and generated a compensating control record.

Impact
Audit risk eliminated
Detection
180ms
Compliance Autopilot
Manufacturing

Export Control Violation — Dual-Use Components Blocked

The Problem

A shipment of precision machining components was being processed for export to a country on the restricted entities list. The components qualified as dual-use items under EAR (Export Administration Regulations).

How It Was Caught

Export compliance engine cross-referenced the material master against the Commerce Control List (CCL), validated the destination country against the Entity List and Denied Persons List, and flagged the dual-use classification. Shipment was held before customs documentation was generated.

Impact
$1.5M fine avoided
Detection
340ms
SupplyChain Prophet
Manufacturing

Supplier Bankruptcy — 47-Day Advance Warning

The Problem

SAP vendor master showed a Tier-2 PCB supplier as active with 92% on-time delivery. No red flags in the system. But the company was 47 days away from filing for bankruptcy.

How It Was Caught

Prophet aggregated 8 external signal sources: 3 quarters of negative cash flow from financial filings, 30% workforce reduction from job posting analysis, credit downgrade from BBB to BB, and a 40% drop in outbound shipments from shipping data. Prophet Score hit 89 — critical risk. Alternative supplier activation began within 72 hours.

Impact
$3.8M disruption avoided
Detection
47 days early
SupplyChain Prophet
Automotive

Shanghai Port Congestion — 21-Day Advance Prediction

The Problem

Critical automotive components were routed through Shanghai port. Historical data showed that port congestion events cause 2-3 week delays, but traditional logistics tools only detect congestion after it occurs.

How It Was Caught

Prophet combined real-time AIS shipping data, weather pattern analysis (incoming typhoon season), geopolitical tension indicators, and port throughput trend analysis. Predicted a major congestion event 21 days before onset, enabling proactive rerouting through Ningbo port.

Impact
$1.6M delay cost avoided
Detection
21 days early
SupplyChain Prophet
Automotive

Lithium Price Spike — 30-Day Forward Contract Opportunity

The Problem

Lithium carbonate prices were about to spike 35% due to a combination of Chilean mine output reduction, Chinese refinery maintenance schedules, and surging EV battery demand. No traditional procurement tool saw it coming.

How It Was Caught

Prophet's commodity intelligence engine correlated mining output data, refinery capacity utilization, demand forecasts from EV production schedules, and futures market sentiment analysis. Generated a 30-day price spike alert with 91% confidence.

Impact
$4.2M saved
Detection
30 days early
VoiceOps AI
Manufacturing

Cement Plant Goods Receipt — 200 Bags via Voice in 3 Seconds

The Problem

Warehouse workers at a cement plant spent 8 minutes per goods receipt — navigating Fiori screens, selecting materials, entering quantities, looking up PO numbers. With 40+ receipts per day, this consumed over 5 hours of productive time.

How It Was Caught

Worker says in Hindi: "Received 200 bags of Birla cement from truck 4827." VoiceOps resolves the material (Birla Cement 50kg OPC), matches to open PO, validates quantity against PO line, and posts the MIGO goods receipt in SAP — all in 3 seconds. No screen. No typing. No training.

Impact
5+ hrs/day saved
Detection
3 seconds
VoiceOps AI
Manufacturing

Warehouse Stock Check — Instant Query in Tamil

The Problem

Supervisors needed to check stock levels before accepting deliveries or planning production. Each stock check required logging into SAP, navigating to MMBE, entering plant/storage location/material. Average time: 4 minutes per query.

How It Was Caught

Supervisor asks in Tamil: "How many bags of Ambuja cement in warehouse 3?" VoiceOps parses the natural language query, maps "Ambuja cement" to the material master, identifies warehouse 3 as storage location 0003, and returns the real-time stock level from SAP in 2 seconds.

Impact
3+ hrs/day saved
Detection
2 seconds
VoiceOps AI
Manufacturing

Maintenance Work Order — Voice-Created in Kannada

The Problem

Maintenance technicians identified equipment issues on the shop floor but had to walk back to a terminal, log in, navigate to IW31, and manually create a work order. Critical issues were often delayed by 30-45 minutes.

How It Was Caught

Technician says in Kannada: "Create maintenance order for conveyor belt B3, priority high, bearing noise detected." VoiceOps maps "conveyor belt B3" to the equipment master, sets priority to 1 (high), adds the symptom text, and creates the PM order in SAP — immediately from the shop floor.

Impact
45 min/order saved
Detection
5 seconds
Aggregate Impact

Combined Results Across All 16 Scenarios

These are not projections. These are measured outcomes from enterprise SAP deployments.

$4.0M+
Total Fraud Prevented
19+
Compliance Violations Caught
3 Major
Supply Chain Disruptions Averted
8+ hrs
Hours Saved Daily
$4.0M+
Fraud Payments Blocked
Across 6 fraud scenarios
15+
Parallel Compliance Checks
SOX, GDPR, FinCEN, EAR
$9.6M
Supply Chain Savings
Predicted 21-47 days ahead
97%
Time Reduction via Voice
8 min to 3 sec per transaction
Cross-Module Intelligence

When Multiple Engines Flag the Same Entity,
Confidence Multiplies

Each engine is powerful alone. Together, they create a detection capability that no single-purpose tool can match.

Cross-Engine Correlation Example

Vendor "Global Tech Supplies"

FraudGuardScore: 72

Invoice threshold gaming detected — $49,800 on a $50,000 limit

Compliance AutopilotScore: 68

SOX SoD violation — same person created vendor and approved invoices

SupplyChain ProphetScore: 61

Vendor credit rating dropped from A to BBB-, cash flow negative 2 quarters

Cross-Module Correlation Result
97
Combined Risk Score
3 independent engines flagged the same vendor. Individual scores (72, 68, 61) were below threshold. Combined score of 97 triggered automatic payment block and escalation.
Risk multiplier: x1.25 + 15 bonus points. Zero configuration required.
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