Author: Konstantin Anissimov, CEO of Currency.com
Compliance is no longer what it used to be. In a 24/7 operating market that spans multiple jurisdictions, payment methods, and protocols, the status quo of ticking boxes and submitting reports feels disconnected from the actual workings of digital finance. When the systems protected by compliance are borderless, decentralized, and constantly changing, compliance must evolve.
For many, the path forward remains unclear. According to a recent industry report, 71% of executives expect financial crime threats to increase by 2025, but only 23% believe their current frameworks are truly practical. The gap between threats and preparedness is widening.
A new approach is beginning to dominate. In the fintech space, compliance is being reimagined as a system layer built into the core, with the current focus on AI—real-time monitoring, contextual screening, and the engine behind trust.
Some believe that the old compliance model did not collapse due to a single flaw but rather broke under cumulative pressure. As digital currencies enter broader financial use, the burden of traditional compliance settings is reflected in every metric—too many alerts, too few insights, and too little time to act.
In 2024, over $40 billion in illegal cryptocurrency transactions were recorded. Meanwhile, sanctions screening remains unstable: 39% of companies say they are confident in detecting violations, and only one-third feel prepared for the rising geopolitical risks. In short, it looks more like a patchwork solution under pressure.
Is there a way to alleviate this pressure? Yes, it starts with embedding compliance into the core of the system. This means fewer dashboards and more upstream decisions made by models that flag and contextualize risks before human involvement.
The result is a gradual transition from human-centered workflows to embedded, AI-driven decision systems. In practice, these tools help map wallet behaviors, explain cross-chain anomalies, and detect mismatches between business logic and regulatory areas at scale in real-time.
Forget the idea of completely replacing compliance teams. Instead, ensure they have the right tools. As this embedded logic finds its place, it is quietly changing the way people interact with digital finance.
If compliance becomes invisible—always on, continuously checking—the next big question is: can users trust a system they no longer see?
As compliance becomes embedded, the user experience changes profoundly, though not always visibly. There are no pop-ups asking you to verify the source of funds, nor sudden freezes from algorithms that do not explain themselves.
From the outside, it feels smoother. However, the smoother it is, the more trust becomes a systemic issue.
When compliance is opaque, even if it is effective, it can create uncertainty. Regulators have begun to push back against companies that exaggerate their AI capabilities, and investors are starting to be skeptical of vague statements. Therefore, efficiency is good—opacity is not.
This is where transparency becomes crucial. Platforms must openly communicate how AI is used, which helps maintain confidence among users and regulators. In the cryptocurrency industry, reputational damage spreads quickly, and trust can only be gained through clarity.
In this context, trust depends on whether the system as a whole is effective. Regardless of agreement, if the underlying infrastructure cannot keep pace with the growing risks, complexities, or regulatory demands, a smooth experience means little.
AI-native compliance must be interoperable, explainable, verifiable, auditable, and capable of handling potential conflicts of rules across jurisdictions. Assembling such a system requires more decisive steps.
If cryptocurrency is serious about making AI-native compliance the norm, architecture is as important as ambition. Currently, most systems are pieced together—one model handles sanctions, another tags wallets, and a third generates alerts.
This setup may work in isolation but cannot hold up under pressure. Platforms must begin to design compliance as an integral part of overall operations. Risk models should communicate with each other, and alert engines must learn from outcomes, which is the way to make decisions understood and improved over time.
Some platforms are already showcasing blueprints. For example, a crypto cybersecurity company recently launched an AI tool to detect wallet "address poisoning," claiming a 97% success rate by analyzing cross-chain behavior contextually. Other major issuers are integrating risk detection, real-time monitoring, and KYC tools directly into their trading tracks.
Additionally, zero-knowledge proof (ZKP) frameworks are being piloted to provide the final missing piece for compliance—privacy-preserving verification. Thus, ZK proofs allow platforms to confirm rule consistency without exposing user identities.
AI-native compliance is a structural choice. Systems embedded with intelligence from the start are setting new benchmarks: faster decisions, fewer false positives, deeper understanding of customers, and workflows that dynamically change risk assessments in real-time.
The industry must embed unified models, transparent logic, and frameworks like ZK proofs to protect users without sacrificing standards to get there. AI will not make digital finance compliant by default. It will provide compliance departments and businesses with the constraints needed to stay ahead.
Opinion author: Konstantin Anissimov, CEO of Currency.com.
Related: Anthropic's valuation soars to $183 billion, Claude AI gains attention in the cryptocurrency space.
This article is for general informational purposes only and is not intended and should not be construed as legal or investment advice. The views, thoughts, and opinions expressed here are solely those of the author and do not necessarily reflect or represent the views and opinions of Cointelegraph.
Original article: Opinion: Without AI-native compliance, cryptocurrency cannot scale
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