Important points
- AI-powered address clustering transforms thousands of fragmented wallets into a consistent entity-level view. This is the basis for any serious cryptocurrency research.
- Graph-based network detection maps illicit infrastructure across multiple hops, enabling rapid response to high-impact events like the US$1.46 billion Bybit breach.
- AI and machine learning (ML) improve detection of suspicious activity by reducing noise, flagging typological matches before full attribution is complete, and presenting reliable alerts for human review.
- TRM’s 2026 Crypto Crime Report found that illegal actors captured 2.7% of available cryptocurrency liquidity in 2025, with AI-powered fraud increasing by nearly 500% year-over-year.
- Responsible use of AI in blockchain intelligence means that AI enhances analyst judgment. AI does not replace analyst judgment. Outputs must be explainable, verifiable, and defensible before being used for enforcement.
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Since their inception, blockchain intelligence tools have been designed with artificial intelligence (AI) and machine learning (ML) capabilities to help users make sense of vast amounts of data. These platforms are used by investigators and compliance teams to identify illicit networks, track hacking proceeds, map sanctions evasion infrastructure, flag suspicious activity, and rely on AI and ML to identify patterns and understand blockchain at scale.
The challenge for investigators and compliance teams is not access to data (public blockchains are radically transparent); Law enforcement, in particular, is rapidly transforming data into structured, actionable intelligence and responsibly using AI and ML-enabled tools.
This post explains how AI works with blockchain intelligence and what responsible deployment looks like in high-stakes law enforcement.
Why raw blockchain transparency is not enough
All transactions on the public blockchain are recorded, timestamped, and accessible to anyone with access to a block explorer. But massive transparency is not the same as clarity. Major blockchains generate millions of transactions every day. Cross-chain bridges, decentralized exchanges, and wrapped assets add complexity that can quickly overwhelm manual reviews.
Blockchain intelligence exists to translate that transparency into usable structures. AI is the engine that enables transformation, not as a feature of the future, but as the mechanism that makes investigation and compliance tools work today.
In an environment where illegal activity will reach record levels in 2025, the real challenge will be prioritization, contextualization, and entity identification at scale.
How AI improves suspicious activity detection
For compliance teams at exchanges, banks, and virtual asset service providers (VASPs), the most immediate impact of AI is in detecting suspicious activity. Specifically, the ability to reduce alert fatigue, surface high-signal risks, and support the filing of defensible suspicious activity reports (SARs) and suspicious transaction reports (STRs). For investigators, it speeds triage and quickly identifies patterns of behavior.
The result is fewer false positives, true risks are escalated more quickly, and documents are more defensible. All of these are important for both regulatory compliance and enforcement outcomes.
Network discovery: Mapping infrastructure, not just transactions
After identifying an illicit wallet or cluster, investigators need to understand its ecosystem: who is funding it, where the proceeds are consolidated, what exchanges and protocols are involved, and whether there are regular liquidity hubs. AI- and ML-enabled blockchain analysis and graphing tools accelerate this scaling process, giving investigators a larger picture of the threat landscape.
TRM Forensics uses graph traversal to evaluate trade amounts, timing correlations, asset trends, and counterparty frequency to reveal reliable paths. Rather than manually tracking hops, researchers can visualize multi-order networks and quickly identify structural nodes.
Network discovery was especially important in 2025. Fraudsters stole US$2.87 billion in nearly 150 hacks, with the Bybit breach alone accounting for US$1.46 billion, or 51% of the annual total. In such events, rapid network detection allows exchanges, stablecoin issuers, and law enforcement to identify consolidation points and act before funds are irreversibly dispersed.
It’s not just about tracking. It’s about identifying the infrastructure.
Behavioral pattern recognition and typology detection
AI-driven systems do more than just map static networks. They identify behavioral characteristics.
Fraudsters exhibit repeatable transactional behavior. Fraud networks exhibit predictable stablecoin routing patterns. Ransomware groups are consolidated through specific liquidity venues. Sanctions evasion infrastructure uses repetitive asset conversion sequences. The ML model detects these signatures throughout the study. If similar patterns surface in new wallet activity, the system can flag potential typological matches before full attribution is complete.
This is especially important given the current threat environment. AI is lowering the barrier to entry for fraud and amplifying the volume of attacks in ways that manual review cannot absorb. TRM observed an approximately 500% increase in AI-powered fraud activity over the past year, with USD 35 billion flowing into cryptocurrency fraud schemes globally in 2025.
As fraudulent content rapidly evolves, including deepfakes, synthetic advisors, and adaptive multilingual outreach, behavioral detection anchored in transactional structures provides more durable signals than content-based moderation. TRM’s Signatures® feature is designed to detect structural fingerprints of known types in real time.
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Combination of on-chain and off-chain intelligence
The use of AI in blockchain intelligence goes beyond graphs. Natural language processing and entity resolution tools integrate open source intelligence (OSINT), sanctions designations, enforcement actions, domain registration data, and threat actor communications.
This convergence enhances attribution. Wallet clusters are connected to on-chain patterns as well as real-world infrastructure and documented enforcement history. The result is not only faster tracing but also more robust contextualization.
Responsible AI: What it means for high-stakes investigations
For public agencies, the question now is not whether to use AI in investigations; How to do it in an explainable and auditable way defensible. This distinction is important because the output of AI is not self-authenticating. Cluster inferences or risk scores only have analytical weight if the underlying methodology can be examined and validated.
Responsible use of AI in blockchain intelligence comes down to a few core principles.
AI augments analyst judgment, not replaces it
Flags and scores are inputs to human analysis, not verdicts. All AI-assisted findings must be traceable to the underlying transaction data before influencing enforcement action.
output must be explainable
Glass box attribution, a feature of TRM’s blockchain intelligence platform that provides full transparency into how attribution is derived, so analysts can see what signals triggered clustering inferences and risk flags, is essential for documentation, internal reviews, and legal defense. Opaque scoring creates downstream risks.
Privacy considerations are built in, not an afterthought.
AI systems operating in enforcement contexts must respect data retention limits, handle PII appropriately, and comply with model governance standards. Where cross-border, this includes the jurisdiction’s data processing rules.
These are not aspirational guardrails. These are operational requirements for public sector teams and compliance programs that must withstand scrutiny from regulators.
AI that cannot be explained cannot be defended, and AI that cannot be defended creates liability for the agencies and companies that use it.
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AI is essential to fighting AI-powered crime
AI is not a speculative addition to blockchain intelligence. It is the foundation of operations. And as adversaries continue to deploy AI to amplify deception and evasion; Defense AI This ensures that blockchain transparency is reflected in the findings and not just the raw data.
The application of AI in blockchain intelligence, used responsibly with human oversight, transparent methodologies, and verification workflows, is one of the most powerful tools available to investigators and compliance teams.
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FAQ
1. How can AI improve suspicious activity detection?
AI improves detection of suspicious activity by training risk models based on known illegal typologies and applying them in real-time. This reduces noise, shows high signal alerts, and prioritizes those that reach human review. Rather than applying static thresholds, AI systems evaluate behavioral patterns (routing sequences, integration behavior, counterparty exposure) that are difficult for bad actors to adapt to. The result is fewer false positives, faster escalation of true risks, and more defensible SAR/STR documentation.
2. What is AI-powered fraud and how can government agencies detect it?
AI-powered fraud uses artificial intelligence to amplify deception. That means deepfake impersonations of financial experts, synthetic investment advisors, and automated multilingual support that adapts to victims in real time. In 2025, TRM observed an approximately 500% increase in AI-powered fraud activity. Detection primarily relies on behavioral analysis of on-chain activities.
3. How do you balance privacy and applying AI?
Balancing privacy and AI enforcement requires designing AI systems with built-in data governance. These include clear data retention limits, appropriate processing of personally identifiable information (PII), jurisdictional compliance in cross-border litigation, and model governance standards that address bias and explainability. For public sector agencies, this also means auditable documentation of how AI output is used in enforcement activities. Privacy considerations are not a constraint to effectively enforcing AI. Requirements for AI to remain under surveillance.
4. What is address clustering in blockchain intelligence?
Address clustering is the process of grouping multiple wallet addresses that are likely controlled by the same entity. AI algorithms analyze behavioral signals such as co-spending patterns, timing, and counterparty overlap to identify statistical associations. The result is entity-level insight. Instead of looking at thousands of individual wallets, researchers can see who controls what.
5. What is the difference between AI in blockchain intelligence and generative AI for investigations?
AI in blockchain intelligence refers to fundamental technical capabilities such as clustering algorithms, graph analytics, and machine learning models for behavioral detection that power investigation and compliance tools. Generative AI is increasingly being used as an interface layer on top of these tools, including network summarization, report creation, and surfacing insights. The two serve different functions and the generated AI output requires validation against the underlying blockchain data before being used for formal investigations or regulatory submissions.
6. What does responsible AI mean for crypto investigations?
Responsible AI in cryptocurrency research means that the output of the AI is explainable (analysts can see the cause of flags and inferences), verifiable (checked against underlying transaction data before use), and auditable (methodology is documented and defensible). This also means that AI augments rather than replaces analyst judgment. High-stakes decisions like flagging a wallet for law enforcement action or filing a SAR require human review and approval. Responsible AI is not a policy position. It is an operational requirement to obtain a valid result in court.
