How NOTO is Redefining Enterprise Financial Crime Management

Financial crime is evolving at an alarming rate, with criminals exploiting faster payments, fragmented systems and increasingly sophisticated tactics. Many financial institutions are still held back by legacy tools that create siloes between fraud and anti-money laundering teams, limiting visibility and slowing response times. 

NOTO was founded to address these challenges through a unified Enterprise Financial Crime Management platform that brings together transaction monitoring, customer risk assessment, sanctions screening and machine learning in a single scalable solution.

Here, CEO and Co-Founder Ivan Stefanov shares how modern platforms can help organisations stay ahead of emerging threats while improving speed, governance and decision accuracy. He also explores the practical role of AI and machine learning in prevention, the importance of explainability and why flexibility is essential as regulatory expectations increase.

Tell us about NOTO's mission and how you help financial institutions combat fraud

At NOTO, our mission is to fundamentally change the way financial institutions prevent fraud and financial crime, such as money laundering. We believe that technology limitations, rigid platforms or fragmented point solutions shouldn’t hold the industry back. 

Instead, organisations should have access to a unified, modular and infinitely scalable platform that adapts to their business. Our goal is transformative but straightforward: create a world where the prevention of fraud and financial crime is no longer constrained by technology, scalability, capability or cost.

NOTO was founded by experts who spent years on the front lines of fraud, risk and compliance, seeing first-hand how legacy, outdated tools were slowing teams down, increasing cost and creating siloes of fraud and compliance functions. 

The company was built to solve these challenges. We designed an Enterprise Financial Crime Management (EFM) platform that brings together all core risk operations – real-time transaction monitoring, customer risk assessment, sanctions screening, account and payment fraud detection, and advanced machine learning – into a single, enterprise-grade solution.

To help institutions combat financial crime we deliver speed (by having the tools to react quickly to new threats) and complete control. With NOTO, teams can map any data in any format, orchestrate risk signals from multiple sources and deploy new rules and models in minutes without relying on external development. 

This flexibility allows organisations to evolve their risk strategy at the same pace as emerging threats.

NOTO unifies fraud prevention and AML into one ecosystem. This creates shared intelligence, centralised case management and a holistic view of customer behaviour and risk. Instead of juggling separate tools, institutions gain a single powerful platform that reduces operational complexity and improves decision accuracy.

Ultimately, our mission is to help organisations stay ahead of financial crime while giving them the confidence, transparency and agility needed to grow safely. 

What key difference between AI and machine learning should financial institutions understand?

AI and Machine Learning (ML) are often used interchangeably, although the terms usually refer to different parts of a spectrum.

AI is a general discipline concerned with creating machines that mimic human cognition, such as solving grand scientific problems or autonomously navigating unpredictable environments while strategically striving to achieve a given goal.

Machine learning is a narrow subfield of AI that uses mathematical models that self-improve at solving a particular task by incorporating data.

Large Language Models (LLMs) are widely labelled as AI, even though they are still far from actual human cognition. The main difference between AI and ML lies in model complexity, associated execution speed and explainability. 

Classical ML models excel at learning patterns from vast amounts of tabular data. LLMs excel at processing unstructured data, such as natural language human input, documents, images and audio recordings, but run much more slowly - specialised hardware is required for the output to be produced within a reasonable time. 

They are better suited for asynchronous or interactive tasks, such as agentic systems that help resolve open fraud or AML cases, expert systems, research assistants, report writers and chatbots. 

When should fintech companies buy a proven solution with built-in ML and AI capabilities vs build their own?

The decision to buy a proven financial crime prevention solution versus building one in house is one of the most consequential choices a fintech company can make. Too often, firms underestimate the complexity, cost and long-term obligations associated with building their own AI-enabled fraud and AML infrastructure.

Building in house can seem tempting, especially for engineering-driven companies or those wanting complete control. 

But in reality, custom-built systems frequently exceed budgets, miss deadlines and create ongoing maintenance burdens that fall disproportionately on fraud and compliance teams. 

These projects require far more than engineering capacity: they demand deep domain expertise, continuous model tuning, data governance, explainability controls and constant upkeep as criminal behaviour evolves. Crucially, building in-house is a permanent programme, not a one-time build.

Fintechs should reserve in-house development for areas that create direct competitive advantage like core product features, payment flows, customer experience and embedded financial services. Fraud and AML infrastructure, by contrast, is a highly specialised discipline that requires dedicated tooling, governance and ML capabilities that mature vendors have refined over many years. 

A proven platform with built-in AI and machine learning is the better choice when:

• You need fast implementation and measurable impact
• You operate at scale and require real-time decisions with sub-100 milliseconds latency
• You must meet regulatory expectations for explainability, auditability and continuous monitoring
• Your teams lack the capacity to maintain models, handle drift or build robust governance frameworks
• You cannot afford the risk of overrun, downtime or technical debt

However, ‘buy’ does not mean surrendering flexibility. The next generation of platforms, including NOTO, offers open data schemas, modular AI and the ability to upload or govern your own models. This gives fintechs the best of both worlds – enterprise-grade capability with customisation where it matters.

Looking ahead, firms should identify platforms that offer on-premise AI, explainable models, hybrid rule-ML engines and instrumentalised AI agents that automate analyst workflows safely and compliantly. 

These innovations will become essential as fraud sophistication grows and regulatory frameworks like the EU AI Act take effect.

What should organisations consider when choosing an enterprise financial crime prevention platform?

It is a strategic decision that determines whether an organisation stays ahead of evolving threats or remains trapped in a cycle of firefighting. 

Based on our experience and field-tested EFM methodology, there are several critical factors institutions should consider when evaluating solutions.

Deep alignment with product flows and risk surface is key. A vendor must support a complete understanding of how products work. Without the ability to map and ingest all relevant data flexibly, even the most advanced tools risk misalignment with actual exposure. As the playbook stresses, designing controls without a precise product map is a major pitfall.

Comprehensive visibility across all fraud and AML risks is important, and organisations should look beyond raw fraud losses. The right platform must provide transparency into direct losses, operational workload, friction and customer impact, partner compliance expectations and reputational or regulatory exposure. A fragmented or single-use-case tool won’t give the enterprise-level clarity needed for strategic decision making.

Look for the ability to model likelihood × impact, and operate with explainability. Effective financial crime prevention requires consistent, comparable risk assessments and ongoing refreshes. Platforms must offer explainable decisions, unified reporting and the ability to forecast trends, not just detect them reactively.

A modern EFM platform must support a multi-layered control architecture across product-level controls, real-time decisioning, ML models, sanctions screening and orchestration of internal and external data sources. 

It should adapt to the ecosystem without forcing rigid schemas or heavy IT dependencies. This architecture is where long-term success is won or lost.

Financial crime evolves constantly, making operational efficiency and continuous improvement key. The selected platform must enable small, rapid iterations, phased implementation and continuous measurement. It should also integrate seamlessly with case management, business intelligence (BI), audit trails and testing environments to support long-term resilience.

Ultimately, organisations should choose a platform that unifies data, decisions and governance across their entire customer journey, empowering them to prevent more financial crime with less friction and lower operational cost. 

NOTO aligns directly with these principles, helping institutions turn this EFM playbook into measurable, day-to-day results.

Which emerging financial crime trends are traditional prevention methods failing to address?

The financial crime landscape is evolving faster than traditional prevention methods can keep up, and several emerging trends are exposing the limitations of legacy systems. 

The most significant shift is happening upstream in the customer journey. With the rise of instant and account-to-account payments, fraudsters are moving earlier, using social engineering, beneficiary manipulation and authorised push payment (APP) scams to trick customers long before a transaction even occurs. 

Conventional rule engines, focused solely on transactional signals, simply cannot capture intent, behavioural anomalies or cross-journey risk. Another fast-growing threat is the proliferation of mule networks and synthetic identities. 

These actors behave “cleanly” for months before activation, making them nearly invisible to static rules or siloed monitoring tools. Traditional systems that treat each account, transaction or channel in isolation cannot identify the relational patterns that expose co-ordinated fraud. Fragmented architectures inside most financial institutions compound these challenges. 

Fraud and AML teams still operate on separate systems, each with its own data pipelines, case management tools and decision logic. This fragmentation leads to duplicate integrations, blind spots, conflicting decisions and high operational costs, all of which slow response times at a time when speed is critical.

This is the problem NOTO set out to solve. We built a single EFM platform that ingests data once, unifies fraud and AML decisioning and applies rules, graph analytics, network intelligence and machine learning across the entire customer journey. 

Native entity resolution surfaces mule networks and counter-party risk, policy-as-code enables rapid iteration and shared data and workflows break down organisational silos. Our clients see measurable uplifts including fewer false positives, faster detection of mule activity, reduced manual reviews, higher legitimate approval rates and dramatically lower vendor and datacentre costs. 

NOTO was built for today’s world: instant, interconnected, adversarial and rapidly-evolving.

How will AI reshape the financial crime prevention landscape in the years ahead?

AI will fundamentally re-shape financial crime prevention, but in ways that are more nuanced, more governed and more human-centred than today’s hype suggests.

The first shift will be the rise of AI as a co-pilot rather than an autopilot. LLMs will dramatically accelerate analyst workflows, summarising lengthy case histories, extracting signals from unstructured data, drafting STR (Suspicious Transaction Report)/SAR (Suspicious Activity Report) narratives, reconciling entities and highlighting inconsistencies across systems – enhancements that will cut investigation times from hours to minutes. 

However, because LLMs can hallucinate and lack deterministic behaviour, human-in-the-loop oversight will remain mandatory. 

The second significant evolution will be a clearer division of labour between AI types. Real-time decision-making will continue to rely heavily on traditional ML, which is fast, explainable and cost-efficient. 

Around this backbone, unsupervised and graph-based models will expose mule networks and coordinated fraud rings. 

In contrast, on-premises foundation models will detect behavioural anomalies that are invisible to rule-based or legacy ML systems. 

The future will not be AI instead of ML, but rather a hybrid architecture combining deterministic rules, real-time ML scoring, network analytics and AI-driven context.

AI will force a profound shift in governance, sovereignty and compliance. With the EU AI Act entering force in 2026 or 2027, high-risk AI systems such as AML engines and credit assessment models will require continuous monitoring for drift, bias, explainability gaps and auditability. 

Financial institutions will prioritise models they can fully trace, justify and version-control. This will push the industry toward sovereign, on-premise AI, where sensitive data never leaves the organisation and AI decisions remain fully compliant, secure and audit-ready.

Meanwhile, criminals will move quickly, using generative models to create multi-lingual phishing messages, voice and video deepfakes, synthetic identities, refund scams and automated account takeovers. This will demand stronger provenance checks, behavioural analytics, cross-data orchestration and faster model iteration cycles.

NOTO expects the winning strategy to be balanced: ML remains the operational backbone,  on-premise AI enhances detection and dramatically accelerates investigations, and transparent governance ensures resilience, trust, and regulatory alignment. 

The future is not ‘AI replacing experts’, it is AI empowering experts to cover more risk, more effectively and with far less friction.

The future will not be AI instead of ML, but rather a hybrid architecture combining deterministic rules, real-time ML scoring, network analytics and AI-driven context.

AI will force a profound shift in governance, sovereignty and compliance. With the EU AI Act entering force in 2026 or 2027, high-risk AI systems such as AML engines and credit assessment models will require continuous monitoring for drift, bias, explainability gaps and auditability. 

Financial institutions will prioritise models they can fully trace, justify and version-control. This will push the industry toward sovereign, on-premise AI, where sensitive data never leaves the organisation and AI decisions remain fully compliant, secure and audit-ready.

Meanwhile, criminals will move quickly, using generative models to create multi-lingual phishing messages, voice and video deepfakes, synthetic identities, refund scams and automated account takeovers. This will demand stronger provenance checks, behavioural analytics, cross-data orchestration and faster model iteration cycles.

NOTO expects the winning strategy to be balanced: ML remains the operational backbone,  on-premise AI enhances detection and dramatically accelerates investigations, and transparent governance ensures resilience, trust, and regulatory alignment. 

The future is not ‘AI replacing experts’, it is AI empowering experts to cover more risk, more effectively and with far less friction.