ComplyControl Q&A: our platform is AI-native from day one, not a traditional tool with AI “sprinkled” on top"

At TheCFOAI, we cover the fast-moving intersections of finance, compliance, and cybersecurity—areas where AI is reshaping how modern CFOs operate. One company pushing that transformation forward is ComplyControl, which was recently recognized as a technology leader in the “Generative AI in Risk and Compliance 2025” report by Parker & Lawrence Research.

I had a Q&A with Roman Eloshvili, the founder of ComplyControl, to explore where AI is taking financial compliance next. Roman brings more than two decades of experience across fintech and banking, including senior leadership roles building solutions for major financial institutions. In 2023, seeing the widening gap between traditional compliance frameworks and AI-driven financial activity, he launched ComplyControl to help institutions operate with real-time risk intelligence rather than backward-looking controls.

The platform uses AI to analyze transactional patterns and account behavior at scale, flag anomalies early, and surface emerging risks before they become costly problems, an approach that mirrors where the industry is heading.

1. Roman, could you share more about what ComplyControl does at a high level, and what specifically led to your recognition in the Parker & Lawrence report?

Certainly. Broadly speaking, ComplyControl is a compliance automation platform that focuses on using AI-based tools in financial crime prevention. We bring together various aspects of compliance, such as transaction monitoring, sanctions screening, anomaly detection, and so on, in one unified system, and make all these processes more streamlined and easier to handle.

Instead of teams spending hours manually reviewing everything, our AI systems do most of the work automatically. Just as one example, they can scan for risks and explain in real time why something was flagged as suspicious. A human compliance officer then simply has to review the AI model’s output and decide how to act on this information.

As for the recognition we got in the Parker & Lawrence Research report — I believe it comes down to two main things: the technology and the philosophy behind our company. We built ComplyControl as an AI-native platform from day one, not as a traditional tool with AI “sprinkled” on top. So when the report highlighted leaders in transaction monitoring, it made sense for them to look for firms that can provide both powerful and accessible services. This combination is what sets us apart and helps us stand as a leader in our industry.

2. You were highlighted for your expertise in transaction monitoring — could you elaborate on what differentiates your approach, and who typically represents your core customer profile?

The core idea behind our approach is that AI should help people along, rather than replace them. Our transaction-monitoring engine is built to work in real time rather than in delayed batches. What this means in practice is that when a transaction comes through, our system analyzes it instantly, cross-checks it against pre-established parameters, and returns a decision and an explanation behind it.

As a result, the time it takes per average check drops from several minutes to just a couple of seconds — a major advantage when most customers these days expect their payments to be near-instant.

Another big edge that ComplyControl has is how customizable our system is. Compliance officers can write rules in plain human language — literally as simple sentences — and the AI turns them into precise logic. You don't need to be a technical specialist or have any in-depth knowledge of coding to keep your monitoring framework up to date. This makes our AI system incredibly adaptable, and when regulations or fraud patterns see a change of some kind, it can be adjusted accordingly with minimal fuss.

We also designed our services to be extremely cost-efficient. In many cases, clients see monitoring costs drop threefold, which can translate to around €25,000 in monthly value — or roughly €300,000 annually. Or, to put it another way, if an organization processes around 50,000 transactions a month, its yearly operational savings can exceed €1.2 million. That is no small cost for a business to save due to modernized workflows.

And as for our customer profile, we typically serve small to medium-sized digital-first banks and fast-growing fintechs in the UK and EU. These companies are often ready and willing to upgrade the quality of their financial services, but they tend to be constrained by legacy systems or tight budgets. Utilizing our solutions helps them scale compliance processes quickly without having to scale team sizes and costs to an unsustainable degree.

3. Our blog frequently covers data fragmentation issues in financial services. How does ComplyControl leverage AI to unify fragmented data sources across the compliance lifecycle?

Data fragmentation is indeed one of the biggest challenges in compliance today, because information sits in different silos — onboarding files, KYC records, customer profiles, transactions, sanctions lists, and so on. All of that data is recorded in different formats and it moves at different speeds. What we’ve done at ComplyControl is use AI as a connective tissue that structures that information and uses it to tie the entire compliance lifecycle together.

Our platform uses advanced natural-language understanding (NLU) to interpret unstructured content like policy documents, regulatory guidelines or adverse-media articles and translate it into a more standardized, machine-readable format. From there, we run automated validation and gap analysis, which is incredibly useful during policy reviews or when navigating licensing requirements. The system can highlight missing elements, inconsistencies, or out-of-date information, making the compliance process a lot clearer and minimizing stumbling blocks for teams.

Once the data is harmonized, our historical ML models start connecting past client behavior and new real-time events, which is essential in upping the quality of detection without being distracted by false positives.

Our team also built privacy-by-design into this layer. Sensitive data is anonymized or otherwise minimized before we feed it to analytical models, which means all data processing happens in accordance with GDPR guidelines.

Of course, merging all the information sources only works if the unified output you get in the end can actually be called trustworthy. And this is where our multi-layer validation comes in. ComplyControl uses both AI and deterministic rule engines to cross-check results and apply confidence scoring to anything uncertain, so that the matter is brought up for human review. This ensures that outputs remain transparent and explainable.

Finally, once everything is consolidated, the platform generates human-readable reasoning, with cited evidence and explained risk patterns. That’s how we also eliminate the “interpretive fragmentation” where two analysts might reach two different conclusions from the same set of data. Instead, everyone gets the same context to work with and the same view of risks.

The way we see it, this is the real value of AI in data fragmentation: it doesn’t just unify the information — it standardizes how it’s understood.

4. As AI capabilities expand, compliance risks evolve alongside them. From your perspective, where is the risk and compliance space heading in 2026 and beyond?

The way we see it at ComplyControl, the most important shift of 2026 will be in terms of compliance becoming more dynamic. Between the growing user expectations and expanding AI capabilities, systems will be required — and capable — of operating in constant motion.

Rules will be updated automatically to adapt to regulatory changes as they happen. Risk models will be adjusted in near real-time based on emerging behavior patterns. Compliance teams will spend far less time reacting and performing checks themselves, and instead become “managers” for AI systems, overseeing their decision-making.

Explainability of AI models will also become non-negotiable — as artificial intelligence becomes more deeply embedded in financial services, regulators will demand precise reasoning behind every action. Black-box systems won’t stand up to scrutiny.

And finally, I think we’ll see a proper convergence between compliance and business growth. Given the growing ease of processes, more companies will unlearn to view compliance as a barrier or a complication they “have to deal with” in order to stay out of trouble. Instead, they will come to see it as a competitive edge, allowing them to operate with confidence and enter new markets faster.