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Fraud Prevention

Detect and Block Onchain Fraud Before Funds Leave

Real-time withdrawal fraud detection that outperforms legacy providers by 4X, stopping fraud, scams, and other schemes before transactions execute.

4
X
scam detection effectiveness vs legacy providers
96
%
scam detection vs 28% from legacy providers
$
3
B+
saved in active incidents to date
4
X
scam detection effectiveness vs legacy providers
96
%
scam detection vs 28% from legacy providers
$
3
B+
saved in active incidents to date

Real-Time Withdrawal Fraud Detection and Automated Pre-Transaction Decisioning

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Scam Network Detection

Continuous tracing and clustering engines scan onchain and Web2 sources in real time, detecting new fraud networks within minutes of deployment to protect your users.

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Cross-Chain Off-Ramp Detection

Multi-hop tracing follows fraud-generated funds as they move across bridges and chains, flagging cash-out attempts at exchanges before funds convert to fiat.

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Automated Fraud Decisioning API

Returns real-time APPROVE or DENY recommendations for withdrawals and transfers, enabling organizations to block fraudulent transactions before funds leave.

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Continuous Real-Time Monitoring

Tracks addresses for reputation changes, monitors fund flows across bridges, and provides direct and indirect (multi-hop) exposure coverage of illicit activity.

Proven in Public. Verified Onchain

EARLY WARNING DETECTION

$3.5M Attack Detected

Hypernative detected the malicious address used to steal $3.5M from Curve Finance's hijacked frontend 2 hours before it reached its first victim. Curve was not a customer at the time. Every dollar lost was entirely avoidable.

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How Fraud Prevention Works

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Real-Time Monitoring

Hypernative continuously monitors transactions and onchain activity across chains and Web2 sources, automatically identifying fraud patterns and mapping them to scam clusters in real time.

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Scam Network Clustering

ML models and graph analysis identify fraud clusters and hidden relationships, combining onchain activity with offchain intelligence sources to confidently flag coordinated bad actors that individual address checks miss.

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Automated Decisioning

When a user initiates a withdrawal or transfer, the API evaluates the destination against real-time threat intelligence and returns an APPROVE or DENY recommendation with evidence.

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Continuous Reputation Updates

Addresses are continuously re-evaluated as new intelligence emerges. Previously clean addresses that become linked to fraud clusters are flagged in real time.

Frequently Asked Questions

What is Hypernative Fraud Prevention?

A real-time withdrawal fraud detection and decisioning product that identifies phishing, scams, pig-butchering, address poisoning, drainer activity and other schemes, blocking fraudulent transactions before funds leave via API-first integration.

How does Fraud Prevention differ from legacy compliance tools?

Legacy tools detect fraud after transactions settle, often weeks later. Hypernative detects in real time before the transaction executes. In benchmarking, 96% of scam addresses were identified vs. 28% by competing providers.

What is scam network clustering?

Proprietary machine learning models and graph analysis that identify fraud clusters and hidden wallet relationships across all onchain activity. These models are able to catch coordinated scams that individual address checks miss.

How does the automated decisioning API work?

When a withdrawal or transfer is initiated, the API evaluates the destination in real time and returns an APPROVE or DENY recommendation with supporting evidence, enabling automated fraud blocking.

What types of fraud does it detect?

Pig-butchering scams, investment fraud, phishing domains, drainer contracts, address poisoning, honeypot tokens, impersonation tokens, DNS hijacking, fake airdrops, coordinated scam networks, and more.

How does it integrate?

A single API endpoint that integrates into any withdrawal, transfer, or payment flow. Supports exchanges, custodians, payment providers, and wallets with real-time response at payment-speed latency.

Can I enhance detection with my own data?

Yes. Fraud Prevention can be enhanced with your own historical data for improved detection accuracy specific to your user base and transaction patterns.