Top 10 AI Tools Detecting Wash Trading And Market Abuse In 2026
Crypto markets have come a long way from the days when suspicious trading activity could simply be dismissed as another bout of volatility. As trading volumes have grown and digital assets have spread across centralized exchanges, decentralized protocols and derivatives markets, the line between genuine market activity and manufactured demand has become increasingly difficult to spot.
Wash trading remains one of the clearest examples. A trader can repeatedly buy and sell an asset, sometimes through connected accounts or wallets, without meaningfully changing their economic position. The result can be an inflated trading volume that makes a token appear far more liquid or popular than it really is. Chainalysis has developed on-chain methods for identifying patterns consistent with suspected wash trading, while noting that behavioral signals alone do not prove intent. Its 2025 research estimated that suspected wash trading could account for as much as $2.57 billion in volume across selected blockchains.
That is where artificial intelligence and advanced behavioral analytics are becoming increasingly important. Modern surveillance systems can examine enormous amounts of order, trade, wallet and market data far faster than a human compliance team ever could. They are not simply looking for unusually high volume anymore. They can compare trading behavior, order-book activity, wallet relationships and activity across different venues to identify patterns that deserve a closer look.
Here are ten platforms standing out in this area.
Solidus Labs
Solidus Labs has built its business around the idea that crypto market surveillance needs to look beyond individual transactions. Its Trade Surveillance platform combines trading behavior with order-book dynamics, funding flows, on-chain activity and other signals to identify potentially abusive activity across fragmented markets.
That approach is particularly relevant to wash trading because the behavior can be difficult to understand when individual transactions are viewed in isolation. Solidus says its technology can identify wash trading alongside spoofing, layering, insider trading, front-running, pump-and-dump activity and cross-venue manipulation.
The platform is designed for centralized and decentralized markets, giving exchanges and other trading venues a way to investigate activity across different products rather than treating each market as a separate island. Its use of behavioral analytics and newer agentic AI capabilities also points to where surveillance technology is heading: from simply generating alerts to helping compliance teams understand why an activity may be suspicious.
Kaiko Market Surveyor
Kaiko approaches market abuse detection from a data-heavy angle. Its Market Surveyor product combines Kaiko’s market data with automated surveillance designed to identify potential abuse across both CeFi and DeFi. The system specifically covers wash trading, spoofing, front-running and other forms of manipulation.
One of Kaiko’s biggest advantages is the breadth of market data behind its surveillance technology. Rather than judging an exchange’s trading activity without context, the platform can compare activity against a wider market picture. That matters when trying to determine whether an unusual volume spike is genuine or simply an isolated anomaly.
Kaiko also emphasizes reducing false positives, which is an increasingly important part of AI-powered compliance. An alert is only useful if analysts can actually investigate it. Too many weak alerts can bury serious cases under a mountain of noise.
The company also strengthened its data and analytics position in 2026 through its acquisition of Amberdata, bringing additional digital asset market and blockchain data capabilities under the Kaiko umbrella.
Eventus Validus
Eventus has become another important name in automated trade surveillance, particularly for exchanges and regulated digital asset venues. Its Validus platform uses machine learning, automation and configurable surveillance procedures to identify suspicious trading behavior and prioritize alerts for investigation.
Wash trading is among the behaviors Validus can monitor, alongside self-trading, spoofing, layering, insider trading and other forms of market manipulation. The platform is capable of ingesting data from numerous sources and can operate in real time, which is important for markets that never really close.
Eventus has also been expanding its footprint in newer markets. In August 2026, STX announced that it had deployed Validus for trade surveillance on its regulated event-contract exchange covering areas including crypto.
The addition of Frank AI to Validus gives the platform another layer of AI-driven analysis, allowing users to query surveillance information in plain language and automate tasks such as alert reviews and reporting.
Nasdaq SMARTS
Nasdaq brings decades of traditional-market surveillance experience into the digital asset conversation through its SMARTS market surveillance technology.
SMARTS uses artificial intelligence and machine learning to identify anomalies, assess risk and prioritize cases across markets. Nasdaq describes its approach as behavioral and risk-based, meaning the system is designed to look beyond simple threshold breaches when determining which activity deserves attention.
That distinction is important in crypto. A sudden increase in trading volume does not automatically mean manipulation. Markets can move sharply because of news, listings, liquidations or genuine investor demand. A surveillance system has to separate those events from patterns that resemble coordinated or artificial activity.
Nasdaq’s technology is therefore more of a broad market-abuse surveillance engine than a crypto-only wash-trading detector. For exchanges and institutions operating across multiple asset classes, however, that broader coverage can be valuable.
Chainalysis
Chainalysis is perhaps better known for blockchain investigations, illicit-finance monitoring and crypto compliance, but its analytical capabilities also extend into market manipulation research.
The company has developed methods for examining on-chain trading patterns that may indicate wash trading. Its analysis focuses on behavioral characteristics such as concentrated activity, repeated transactions and unusual trading relationships rather than claiming that a particular wallet is definitively manipulating a market.
That distinction is important. Blockchain data can reveal what happened, but it does not always reveal why it happened. A cluster of wallets trading repeatedly with one another may be suspicious, but investigators still need additional information before concluding that the activity was deliberately designed to manipulate a market.
Chainalysis’ strength is the ability to connect those trading patterns with broader blockchain intelligence. That can help investigators move from an isolated suspicious transaction to a wider picture of wallets, entities and fund flows.
TRM Labs
TRM Labs takes a similarly intelligence-focused approach. Its platform combines blockchain data, attribution, behavioral intelligence and AI-assisted investigations to help organizations identify suspicious activity across the crypto ecosystem.
TRM’s behavioral capabilities are particularly useful when suspicious trading activity crosses multiple wallets or networks. Instead of relying exclusively on transaction values, investigators can examine how funds move, which entities interact with one another and whether certain behaviors match known risk patterns.
This makes TRM useful for the investigative side of market abuse detection. A surveillance alert may identify something unusual, but blockchain intelligence can help establish whether apparently separate wallets are connected or whether activity forms part of a broader scheme.
As decentralized markets become increasingly fragmented, that ability to follow behavior across chains is becoming more important.
Elliptic Investigator
Elliptic is another established blockchain intelligence provider that has moved further into automated behavioral detection.
Elliptic’s Investigator platform can automatically identify suspicious wallet behaviors, including fraudulent NFT orders and wash trading, using on-chain clues. The company says its behavioral detection technology can flag patterns associated with multiple types of crypto scams and other suspicious activity without requiring investigators to manually sift through every transaction.
That capability is particularly relevant to markets where wash trading is conducted through wallets rather than conventional exchange accounts. Investigators can examine whether activity is concentrated among connected addresses, whether assets move in circular patterns and whether trading behavior resembles known manipulation techniques.
Elliptic has also highlighted the broader market-manipulation risks facing tokenized assets, noting that unusual coordinated buying, circular trading and rapid accumulation followed by selling can provide useful behavioral signals.
NICE Actimize SURVEIL-X
NICE Actimize’s SURVEIL-X is designed for a much wider financial-market environment, but its AI capabilities make it relevant to firms dealing with digital assets as well.
The platform uses anomaly detection, machine learning and other analytics to identify suspicious trading patterns. Its market-surveillance tools can detect scenarios including wash trading, front-running, insider dealing and other forms of market abuse.
What makes SURVEIL-X interesting is the emphasis on reconstructing events and understanding behavior rather than simply firing alerts whenever a rule is breached. The system can examine activity across products and markets, which can be useful when manipulation involves related instruments or multiple venues.
NICE also added generative AI capabilities to its surveillance technology, with the company saying the technology can help reduce false positives and improve the identification of genuine misconduct risk.
Scila Surveillance
Scila has been developing AI-assisted surveillance for financial markets for years and now supports digital assets alongside traditional asset classes.
Its Scila Surveillance platform offers real-time monitoring, AI alerting, data analytics and more than 120 configurable alert rules. The company describes its approach as explainable AI, with an emphasis on giving analysts context around why a particular sequence of trading activity was considered unusual.
That explainability matters because market surveillance is not simply a race to find anomalies. Compliance teams may eventually need to explain an alert to regulators, auditors or an internal investigation team. A black-box model that produces a suspicious score without meaningful context can therefore be difficult to rely on.
Scila also explicitly supports digital assets, making it one of the more versatile options for firms that want surveillance across traditional and crypto markets from the same infrastructure.
Aquis Market Surveillance
Aquis Exchange has also invested in artificial intelligence and machine learning for market surveillance. Its surveillance technology monitors order-book activity in real time and generates alerts around disorderly trading and potential rule breaches.
The company has separately discussed its work with the University of Derby on an AI and machine-learning surveillance system designed for anomaly detection and market monitoring.
Aquis’ technology is not built solely around crypto, but that may actually be part of its appeal for institutional operators. Digital assets increasingly resemble other electronically traded markets in the way algorithms, high-frequency strategies and fragmented liquidity interact. Surveillance systems that can process large quantities of order-book data therefore have a natural role to play.
For wash trading investigations, the ability to reconstruct an order’s lifecycle and examine submissions, modifications, cancellations and executions can provide valuable context around suspicious activity.
The Bigger Role of AI in Crypto Market Surveillance
The rise of these tools reflects a broader change in how the industry thinks about market abuse. Wash trading is no longer simply a matter of spotting an unusually high volume figure and assuming something is wrong. Sophisticated surveillance requires a much wider view of market behavior.
The strongest systems increasingly combine several layers of information. Order-book movements can be compared with actual executions. Trading patterns can be connected to wallet activity. Activity on one exchange can be compared with what is happening elsewhere. Historical behavior can also be used to determine whether a particular trader or wallet is behaving unusually.
AI is particularly useful because the amount of information involved is simply too large for humans to process manually. Machine-learning models can sift through millions of transactions and identify patterns that would otherwise take analysts days or weeks to uncover.
Still, AI does not eliminate the need for human judgment. A suspicious pattern is not automatically proof of market abuse. Chainalysis, for example, makes this distinction explicitly in its wash-trading research, noting that behavioral analysis identifies patterns rather than proving intent.
That may ultimately be the most important role for these platforms. Rather than replacing investigators, they give compliance teams a way to concentrate their attention where it matters most. As crypto markets become deeper, faster and more interconnected, that combination of machine-scale analysis and human judgment is likely to become a central part of keeping digital-asset markets credible.
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About The Author
Alisa, a dedicated journalist at the MPost, specializes in crypto, AI, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
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Alisa, a dedicated journalist at the MPost, specializes in crypto, AI, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
