Opinion: Konstantin Anissimov, Global CEO of Currency.com
Compliance is not the same as before. In a market that runs 24/7 across multiple jurisdictions, payment methods and protocols, we feel that the current state of checkboxes and filing reports is separate from how digital finance actually works. Compliance must evolve when the systems you protect are borderless, decentralized and constantly moving.
For many, the future path is still unknown. Recent industry reports show that 71% of management expect financial crime threats to rise in 2025, but only 23% believe the current framework is truly practical. The gap between threat and preparation is growing.
A new approach is beginning to take hold. Compliance is being rethinked as a system layer built into the core across FinTech. Currently, AI is the center of attention. The engine behind real-time monitoring, context screening and trust.
Compliance stacks are embedded from the manual
Some believe that older compliance models are buckled from a single defect, but cracked under accumulated strain. As digital currencies move towards broader economic use, the burden of legacy compliance setups is displayed in all metrics.
In 2024, more than $40 billion in illegal crypto transactions were recorded. Meanwhile, sanction screening remains unstable. 39% of companies say they are confident in their ability to detect violations, with only a third of their feelings in preparation for an increase in geopolitical risk. Simply put, it looks like patchwork under pressure.
Is there a way to get through the tension? Yes, it starts with embedding compliance into the core of the system. This means fewer dashboards are achieved by models that flag and contextualize risk before human involvement, and there are more upstream decisions.
As a result, there is a gradual transition from human-centric workflows to embedded, AI-driven decision systems. In practice, these tools can help you map wallet behavior, interpret anomalies between chains, and detect discrepancies between business logic and regulatory zones at scale in real time.
Forget the idea of replacing your compliance team completely. Instead, make sure you have the right tools. When this built-in logic finds its location, how people interact with digital finance quietly changes.
If compliance is invisible – Always on and always check – The next big question is that users can trust a system that no longer see.
An invisible system requires visible accountability
Once compliance is built in, the user experience changes in deep and important ways, not always visible. There are no pop-ups asking you to confirm your funding source. Nor will it suddenly freeze from an algorithm that flags it doesn't explain itself.
From the outside it feels smooth. But the smoother it becomes, the more trust becomes a system issue.
If compliance is unclear, even if it is effective, it can create uncertainty. Regulators are already beginning to push back against companies that exaggerate AI capabilities, and investors are beginning to treat vague claims with suspicion. Therefore, efficiency is good – opacity is not.
This is where transparency is most important. Platforms need to openly communicate how to use AI. This helps maintain user and regulator trust. In the crypto industry, where reputational damage is rapidly spreading, trust is only gained through clarity.
In this case, trust will depend on whether the entire system works or not. Whether you agree or not, a smooth experience makes little sense if the infrastructure behind it cannot keep up with an increased risk, complexity, or regulatory demand.
AI-Native compliance must be interoperable, explainable, verifiable, auditable, auditable, and auditable to handle potentially conflicting rulesets beyond jurisdiction. And assembling that kind of system means a more critical step.
Creating AI compliance work starts with rules rather than code
When Crypto is serious about making AI-Native compliance a norm, architecture is just as important as ambition. Nowadays, most systems are stitched together. One model handles sanctions, another flag wallet, and the third model generates an alert.
That setup may work on its own, but it cannot withstand under pressure. The platform must begin to design compliance as the overall operating layer to advance. Risk models need to be discussed with each other, but alert engines need to learn from the results. It is a way to understand and improve decisions over time.
Some platforms already show blueprints. For example, one Crypto cybersecurity company recently launched an AI tool to detect “address addiction” in wallets, claiming a success rate of 97% by analyzing behavioral contexts across the chain. Other large publishers have integrated tools for risk detection, real-time monitoring and KYC directly into transaction rails.
Beyond these, the Zero-Knowledge Proof (ZKP) framework is steered to provide compliance with validation of the final missing piece, namely privacy presentation. As a result, ZK Proof allows the platform to check the rule alignment without exposing the user's identity.
AI-Native compliance is a structural choice. The system that embedded intelligence from the beginning has already set a new baseline. It's dynamic to change faster decisions, fewer false positives, deeper customer understanding, and risk assessment in real time.
Industry needs to embed frameworks that protect users without sacrificing standards, such as integrated models, transparent logic, and ZK proofing. AI is not digital finance compliant by default. It provides compliance departments and businesses with constraints to stay ahead of the curve.
Opinion: Konstantin Anissimov, global CEO of Currency.com.
This article is for general informational purposes and is not intended to be considered legal or investment advice, and should not be done. The views, thoughts and opinions expressed here are the authors alone and do not necessarily reflect or express Cointregraph's views and opinions.
