AI-First Compliance Automation for Financial Crime: An Alternative to Manual Reviews (The Demo Room #15)
- Michael Lawrence
- 21 hours ago
- 4 min read
Welcome to The Demo Room – your front-row seat to the future of RegTech, RiskTech, and AI innovation.
In this series, we document our research interviews with the most forward-thinking vendors tackling the industry's biggest challenges. Each blog is built around a comprehensive product demo, providing clear insights into how these innovations address industry challenges.
On this occasion, we met with Mikhail Dunaev, Chief AI Officer of ComplyControl, to discuss how they are building AI-first compliance automation for financial crime, including real-time transaction screening and anomaly detection.
Parker & Lawrence’s latest survey data suggests what many in the market already sense: financial crime is the risk domain where GenAI is producing the strongest financial upside. Financial crime is the top-ranked area of GenAI impact for almost a third of respondents, and half place it in their top three priorities for value creation. That reflects how quickly the economics of financial crime have outgrown legacy tooling.
Zooming out, the global numbers are stark. Nasdaq’s 2024 Global Financial Crime Report estimates that around $3.1 trillion in illicit funds flowed through the financial system in 2023 alone, including roughly $783 billion linked to drug trafficking, $347 billion to human trafficking and $11.5 billion to terrorist financing, while fraud scams and bank fraud schemes added another $486 billion in projected losses. Despite huge investments in controls, only around 1% of suspicious transaction reports are actually investigated worldwide and an even smaller fraction of criminal proceeds are ultimately confiscated.
The typologies driving these losses are increasingly digital and fast-moving. Nasdaq’s research highlights fraud on real-time and faster payment systems, complex mule networks and cross-border organised crime as top concerns for anti-financial-crime professionals. Europol’s 2024 Internet Organised Crime Threat Assessment points to online and payment-card fraud schemes as a growing threat, with millions of EU citizens targeted and billions of euros stolen each year. KPMG and others note that the rapid shift to digital wallets and instant payments has amplified exposure to APP scams, account takeover, sanctions breaches and money-laundering risks, as criminals weaponise the same UX and speed advantages that make these channels attractive to customers.
Against that backdrop, it is not surprising that standard rules-based monitoring is under pressure. The volume and variety of alerts generated by legacy systems are colliding with a threat landscape defined by real-time payments, cross-border mule networks, synthetic identities and AI-enabled fraud. This is precisely where firms are experimenting most aggressively with GenAI and advanced analytics, not just to detect more crime, but to cut false positives and investigative drag at scale, and to extract more value from every alert that is already being generated.
The Problem for Firms
Most institutions are operating with infrastructure designed for yesterday’s risk landscape:
Legacy systems treat transaction patterns, sanctions signals, and external intelligence as separate pipelines.
Detection logic is rigid: adjusting rules requires technical staff and slow governance cycles.
Analysts spend disproportionate time reconstructing context rather than assessing risk.
The result is a system optimised for volume, not intelligence, and one that produces huge operational drag. ComplyControl’s own research estimates that 40% of alerts from legacy transaction monitoring systems are false positives, with analysts often spending 10 minutes per review, creating thousands of wasted hours per month before any investigation truly starts.
This is the gap ComplyControl is targeting with AI.
A Solution: AI-First Compliance Automation for Financial Crime
ComplyControl is growing rapidly, but the maturity of its architecture already stands out: it was built AI-first, not AI-retrofit. Three elements define the product:
Real-time detection across structured and unstructured data
ComplyControl blends:
behavioural transaction patterns
sanctions screening
adverse media
regulatory interpretations
…into a single alerting layer. This is notoriously hard to achieve at scale, especially when unstructured media is involved, yet the platform supplies contextual explanations linking evidence directly to its conclusions.
Plain-language rule creation powered by AI
One of its most distinctive capabilities is democratizing rule authoring. Compliance officers write rules in natural language, and the AI converts them into executable logic. The system visually exposes each building block, ensuring transparency rather than “black-box” automation.
This echoes a broader shift we're tracking across RegTech: using AI to lower barriers to contribution rather than replace expert judgement.
Explainability through confidence scoring
Every alert carries a clear confidence score. Institutions can set operational thresholds aligned to their risk appetite, turning abstract conversations about “AI trust” into concrete governance parameters.
Parker & Lawrence's View
ComplyControl enters a crowded market dominated by well-resourced incumbents, but its execution speed, architectural choices, and measurable performance put it firmly on the radar.
We expect strong traction among European banks and fintechs where compliance teams are lean, transaction volumes are growing, and the economics of false positives are becoming unsustainable. Especially given ComplyControl’s attractive commercials:
90% reduction in false alerts (from 40% to 4%)
2× faster analyst reviews
3,376 analyst hours freed each month — worth approximately €101,000
Pricing of around €25,000/month for the transaction monitoring tool
At the same time, the product is tapping into three structural shifts driving the next phase of RegTech. First, GenAI is moving beyond simple automation towards decision-support, enriching alerts with contextual intelligence — from retrieval to summarisation to hypothesis generation — which aligns squarely with the capabilities compliance teams tell us they value most. Second, explainability has become a differentiator, and ComplyControl’s confidence scoring, transparent rule construction and evidence-linked reasoning meet the expectations supervisors are beginning to set. Third, challenger banks and high-growth fintechs are prioritising fast deployment, local model options and intuitive interfaces for non-technical staff. These are exactly the attributes ComplyControl has optimised for.
It is still early-stage, and scaling against large, data-rich incumbents will require continued momentum. But the breadth of the product signals a team capable of rapid innovation at a time when institutions are increasingly willing to modernise core financial crime tooling.
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