Fraud Detection for High-Volume Streams

Machine learning models trained on billions of on-chain transactions detect deviations from normal user behavior, identifying wash trading, pump-and-dump schemes, and novel attack vectors.

Identifies clusters of accounts controlled by a single entity through advanced graph analysis, preventing airdrop farming, governance manipulation, and vote stuffing.

Continuously scans for known and emerging smart contract vulnerabilities (reentrancy, flash loan attacks) and monitors for suspicious interaction patterns with high-value contracts.

Tracks fund movement across chains and protocols to uncover money laundering, mixing service usage, and off-ramping to fiat, ensuring regulatory compliance.

Analyzes user interaction patterns (keystroke timing, transaction signing habits) to detect account takeover and compromised wallets, adding a layer of identity assurance.

Deploy and manage your own detection logic without code. Create rules for specific token pairs, volume thresholds, or geographic restrictions with a visual editor.

Detect and block fraudulent transactions—including MEV bots, flash loan attacks, and wash trading—within sub-second latency, preventing financial loss before it occurs.

Automate manual review processes with our ML-driven detection models. Free your engineering team from false positive triage and focus on core product development.

Protect your users' assets and data with enterprise-grade security. Build platform credibility and reduce churn by demonstrating proactive fraud prevention.

Our pre-execution analysis filters out malicious transactions, saving gas fees on failed or reverted operations and improving overall network efficiency for legitimate users.

Maintain detailed, immutable audit trails of all flagged activity and risk assessments. Simplify compliance reporting for AML/KYC and future regulatory frameworks.

Our system scales horizontally to handle millions of transactions per day without degradation. Future-proof your application as user volume and transaction complexity grow.

We analyze your transaction patterns, define risk parameters, and establish key fraud indicators (KFIs) specific to your protocol and user base. This phase delivers a clear threat model and detection strategy.

Our engineers build and deploy a custom detection engine using real-time stream processing (e.g., Apache Flink/Kafka). We integrate directly with your RPC nodes and data sources for seamless operation.

We implement and fine-tune detection rules (heuristic & ML-based) against historical and live data. This includes backtesting for accuracy and minimizing false positives to ensure operational efficiency.

We deploy the system into your production environment with full observability dashboards, alerting, and an SLA. Ongoing support includes rule updates and performance reviews.