Market Abuse Surveillance

Continuously scans the mempool and newly mined blocks to identify suspicious patterns as they form. This includes detecting front-running, sandwich attacks, and wash trading by analyzing transaction ordering, gas fees, and wallet relationships in real-time.

Uses statistical models and machine learning to establish baselines for normal trading activity. Flags deviations such as:

• Volume spikes on low-liquidity assets
• Circular trading between related addresses
• Spoofing (large orders placed and quickly canceled)
• Abnormal price impact from small trades

Groups multiple wallet addresses controlled by a single entity by analyzing funding sources, transaction patterns, and smart contract interactions. This is critical for identifying coordinated abuse, such as pump-and-dump schemes executed across dozens of seemingly unrelated wallets.

Tracks asset flows and behavioral patterns across multiple blockchain networks (e.g., Ethereum, Arbitrum, Solana). This prevents bad actors from evading detection by fragmenting activity across chains and is essential for monitoring bridged assets and cross-chain arbitrage strategies that could be abusive.

Specifically monitors Automated Market Maker (AMM) pools for manipulation tactics like:

• JIT (Just-In-Time) liquidity attacks
• Oracle manipulation via flash loans
• Tick manipulation in concentrated liquidity pools (e.g., Uniswap V3) Analyzes pool reserves, price ticks, and LP positions to identify artificial price movements.

Encodes known market abuse typologies from traditional finance (TradFi) for on-chain contexts. This includes detecting patterns analogous to insider trading (trading ahead of a governance vote or protocol upgrade) and marking the close (manipulating an asset's price at the end of a reporting period).

The foundational layer, consisting of immutable transaction records from the blockchain itself. This includes wallet addresses, token transfers, smart contract interactions, and timestamps. Analysts use this to trace fund flows, identify related wallets (clustering), and detect patterns like circular trading or rapid, high-volume transfers between controlled accounts.

Real-time data from centralized and decentralized exchanges, including:

• Limit order books (price, size, side)
• Trade execution feeds (fills, cancellations)
• Market depth and spread This data is critical for detecting spoofing (large non-bona-fide orders), layering, and quote stuffing, which manipulate perceived supply and demand.

The pool of pending transactions before they are confirmed in a block. Surveillance systems monitor the MemPool to see intent, including:

• Front-running opportunities where an actor sees a pending large trade.
• Time-bandit attacks where miners/validators reorder transactions.
• Failed or replaced transactions, which can signal testing of market conditions.

Decoded event logs emitted by smart contracts (e.g., Swap, Transfer, Mint). This provides semantic context to raw transactions, revealing actions within DeFi protocols like liquidity pool swaps, loan liquidations, or governance votes. Essential for detecting abuse specific to Automated Market Makers (AMMs) and lending platforms.

External data correlated with on-chain activity to provide motive and context. Includes:

• Social media sentiment and announcements from Twitter, Discord, Telegram.
• News feeds and regulatory filings.
• GitHub commits for protocol changes. This helps link market movements to specific events or coordinated pump-and-dump campaigns.

Operational data from blockchain nodes and consensus participants. This includes:

• Block proposal order and timing.
• Validator voting patterns and slashing events.
• Network latency and peer connections. Used to detect consensus-level manipulation, such as validator collusion for maximal extractable value (MEV) exploitation or network partitioning attacks.

Banks, broker-dealers, and asset managers with crypto divisions (e.g., Fidelity, Goldman Sachs) use surveillance tools to extend existing market conduct and MiFID II compliance frameworks to digital assets. They monitor for cross-asset manipulation and ensure employee trading adheres to strict internal policies.

Entities like the U.S. Securities and Exchange Commission (SEC), Commodity Futures Trading Commission (CFTC), and Financial Conduct Authority (FCA) use surveillance for enforcement. They analyze market data to identify patterns of abuse, build cases, and issue fines. The Department of Justice (DOJ) may use similar analysis in criminal investigations.

Leading DeFi protocols and their governing Decentralized Autonomous Organizations (DAOs) are increasingly adopting on-chain surveillance to protect their ecosystems. They monitor for flash loan attacks, oracle manipulation, MEV exploitation, and token pump-and-dump schemes within their liquidity pools and governance mechanisms.

Sophisticated trading firms employ surveillance both defensively and offensively. They use it to:

• Detect manipulation targeting their own positions.
• Identify anomalous market activity that signals risk or opportunity.
• Ensure their own algorithmic trading strategies do not inadvertently violate exchange rules or market abuse regulations.

Blockchains generate an immense, continuous stream of on-chain data. Effective surveillance must process millions of transactions per day across multiple networks in real-time to detect abuse as it occurs. This requires high-throughput data ingestion pipelines and scalable infrastructure to handle events like NFT mints, DEX swaps, and liquidations without lag.

While transactions are public, actors are represented by wallet addresses, not legal identities. Surveillance systems must perform entity clustering to link related addresses (e.g., funded from the same exchange deposit) and identify Sybil attacks or coordinated groups. This involves analyzing funding sources, smart contract interactions, and off-chain data leaks.

Abusers operate across multiple blockchains and trading venues (CEXs, DEXs, NFT marketplaces). A wash trade might start on a DEX on Ethereum, move funds via a cross-chain bridge, and conclude on a CEX. Surveillance must aggregate and correlate data across these fragmented liquidity silos to see the full picture, a challenge known as market fragmentation.

New manipulation techniques constantly emerge, particularly around Maximal Extractable Value (MEV). Surveillance must detect sophisticated strategies like:

• Sandwich attacks: Frontrunning and backrunning a victim's transaction.
• Time-bandit attacks: Reorganizing blocks to steal assets.
• Liquidation cascades: Triggering multiple positions for profit. Detection rules must be continuously updated to address these novel on-chain mechanics.

Manipulation can be baked into smart contract logic itself, such as in rug pulls or honeypot tokens. Surveillance tools must analyze contract code (e.g., for hidden mint functions, transfer taxes, or ownership centralization) and monitor for suspicious deployment patterns. This requires integrating static analysis and runtime behavior tracking alongside transaction monitoring.

Many on-chain actions exist in a regulatory gray area. Is a large DEX swap price manipulation or legitimate trading? Systems generate thousands of alerts requiring manual investigation by analysts who must understand both finance and blockchain technology. Defining clear, actionable risk thresholds and reducing false positives is a major operational burden for compliance teams.