Opinion by: Konstantin Anissimov, Global CEO at Currency.com
Compliance isn’t what it used to be. In a market that runs 24/7 across multiple jurisdictions, payment methods and protocols, the status quo of checking boxes and filing reports feels disconnected from how digital finance actually works. Compliance must evolve when the system it protects is borderless, decentralized and constantly moving.
For many, the way forward is still unclear. According to a recent industry report, 71% of executives expect financial crime threats to increase in 2025, yet only 23% consider their current frameworks genuinely practical. The gap between threat and readiness is widening.
A new approach is starting to take hold. Across fintech, compliance is being rethought as a system layer built into the core, and right now, the center of attention is AI — the engine behind real-time monitoring, contextual screening and trust.
The compliance stack is turning from manual to embedded
Some think the old compliance model is buckling from a single flaw but cracking under accumulated strain. As digital currencies move into broader financial use, the burden on legacy compliance setups shows in every metric — too many alerts, too few insights and too little time to act.
In 2024, over $40 billion in illicit crypto transactions were recorded. Meanwhile, sanctions screening remains shaky: 39% of firms say they’re confident in their ability to detect violations, and only a third feel prepared for rising geopolitical risk. Simply put, that does look more like a patchwork under pressure.
Is there a way through the strain? Yes, and it starts with embedding compliance into the system’s core. That means fewer dashboards and more upstream decisions by models that flag and contextualize risk before a human gets involved.
The result is a gradual transition from human-centered workflows to embedded, AI-powered decision systems. In practice, these tools help map wallet behavior, interpret anomalies across chains and detect mismatches between business logic and regulatory zones in real time and at scale.
Forget the idea of replacing compliance teams altogether. Instead, make sure they have adequate tools. As this embedded logic finds its place, it’s quietly changing how people interact with digital finance.
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If compliance becomes invisible — always on, constantly checking — the next big question is: Can users trust a system they no longer see?
Invisible systems demand visible accountability
As compliance becomes embedded, the user experience changes in ways that matter deeply, not always visible. There’s no pop-up asking you to verify your source of funds, or no sudden freeze from a flagging algorithm that doesn’t explain itself.
From the outside, it feels smoother. The smoother it gets, however, the more trust becomes a question of systems.
When compliance is opaque, even if it’s effective, it can create uncertainty. Regulators have already started pushing back against firms overstating their AI capabilities, and investors are beginning to treat vague claims with suspicion. So, efficiency is good — opacity isn’t.
This is where transparency matters most. Platforms must openly communicate how AI is used, which could help retain user and regulator confidence. In the crypto industry, where reputational damage spreads fast, trust is earned only through clarity.
Trust, in this case, depends on whether the system works as a whole. Agree or not, smooth experiences mean little if the infrastructure behind them can’t keep up with growing risk, complexity or regulatory demands.
AI-native compliance has to be interoperable, explainable, verifiable, auditable and built to handle potentially conflicting rulesets across jurisdictions. And assembling that kind of system means more decisive steps.
Making AI compliance work starts with rules, not code
If crypto is serious about making AI-native compliance the norm, architecture matters as much as ambition. Currently, most systems are stitched together — one model handles sanctions, another flags wallets and a third generates alerts.
That setup may work in isolation, but doesn’t hold up under pressure. Platforms must start designing compliance as a holistic operating layer to move forward. Risk models should talk to each other, while alerting engines must learn from outcomes, and that’s the way to have decisions understood and improved over time.
Some platforms are already showing the blueprint. For example, one crypto cybersecurity firm recently launched an AI tool to detect wallet “address poisoning,” claiming a 97% success rate by analyzing behavioral context across chains. Other large issuers are integrating tools for risk detection, real-time monitoring, and KYC directly into their transaction rails.
Beyond these, zero‑knowledge proof (ZKP) frameworks are being piloted to give compliance the final missing piece — privacy-preserving verification. As a result, ZK-proofs allow platforms to confirm rule alignment without exposing user identities.
AI-native compliance is a structural choice. Systems that embed intelligence from the start are already setting a new baseline: faster decisions, fewer false positives, more profound understanding of the customers and workflows that are dynamic to changing the risk assessment in real time.
The industry must embed unified models, transparent logic and frameworks like ZK-proofs that protect users without sacrificing standards to get there. AI won’t make digital finance compliant by default. It will give compliance departments and businesses the constraints to stay ahead of the curve.
Opinion by: Konstantin Anissimov, Global CEO at Currency.com.
This article is for general information purposes and is not intended to be and should not be taken as legal or investment advice. The views, thoughts, and opinions expressed here are the author’s alone and do not necessarily reflect or represent the views and opinions of Cointelegraph.
