Fraud Case Study Simulations
Real-world fraud scenarios detected by NeuroChain AI — sample investigation reports
Total Fraud Prevented
₹2.47 Cr
Avg Detection Time
< 50ms
Cases Detected
5
Detection Accuracy
94.7%
How NeuroChain Catches Fraud — Simple Explanation
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NBFC Sends Data
Loan application hits the API
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AI Brain Checks
GNN scans entire network history
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Instant Decision
Approve, flag, or block in <50ms
Select a Case to Investigate
What NeuroChain Detects — Fraud Type Reference
Complete detection capabilities with example indicators
| Fraud Type | How It Works | Traditional Detection | NeuroChain Detection | Speed |
|---|---|---|---|---|
| Duplicate Borrower | Same person borrows from multiple NBFCs using name variations | Not detected (siloed data) | Device fingerprint + GNN graph + behavioral match | <50ms |
| Velocity Attack | Rapid micro-loan applications below review thresholds | Manual audit (days later) | Real-time rate limiter + network velocity analysis | <50ms |
| Synthetic Identity | Fake identity using real data elements (deceased PAN) | Passes KYC checks | Behavioral biometrics age-pattern mismatch | <50ms |
| Collusion Ring | Insider (loan officer) + fraud ring with shell borrowers | Audit (months later) | GNN graph clustering + approval pattern anomaly | Batch |
| Fake Loan / Property | Same collateral pledged at multiple NBFCs with inflated values | Not detected (no cross-check) | Cross-NBFC collateral registry + market rate analysis | <50ms |
| Money Laundering | Layered transactions to obscure fund source | Manual STR filing | Bloom filter watchlist + pattern detection + auto-STR | <50ms |