AML Toward 2030: More than 3.8 million companies will adopt prevention systems

A recent study by Juniper Research estimates that by 2030 more than 3.8 million companies will operate with third-party AML systems. It is not just numerical growth: it indicates that prevention is no longer a sector-specific requirement but is becoming a horizontal capability across the digital ecosystem.

This leap can be explained by three structural reasons:

1. Stricter Regulation and a Broader Scope

Regulators have expanded the definition of which companies must monitor and report suspicious activity. Money laundering is no longer concentrated in financial services; digitalization and new consumer patterns have shifted illicit activity toward non-financial environments.

This is why sectors now entering the regulatory radar include:

• real estate
• e-commerce and marketplaces
• delivery and gaming platforms
• crypto and digital assets
• real estate firms and payment aggregators
• legal and accounting firms
• businesses with high cash flow

All participate—directly or indirectly—in the movement of money, digital assets, or financial data. The logic is to close vulnerable points: any business that processes payments or intermediates transactions can facilitate illicit schemes if it lacks basic controls. Hence the expansion of the AML/CFT perimeter.

2. New money laundering and fraud typologies

The study highlights techniques that are already part of the day-to-day reality for risk teams:

• Chain hopping and crypto mixers: funds moving across blockchains or mixed with legitimate transactions to lose traceability.
• Cross-border digital mule networks: coordinated structures that move money within minutes using third-party accounts.
• Real-time deepfakes: synthetic voices and faces capable of bypassing identity checks and video calls.
• Origin obfuscation using VPNs, proxies, and encrypted networks: falsified locations and behavioral patterns to evade detection.

The speed and sophistication of these techniques are surpassing the capacity of traditional risk models.

3. AI as the foundation of modern monitoring

The industry standard is shifting toward capabilities that can anticipate anomalous behavior:

• Behavioral analytics: models that learn normal patterns and detect previously invisible deviations.
• Real-time detection: alerts triggered during the operation, not afterward.
• Adaptive models: capable of incorporating new typologies without manual rule-writing.
• Graph analytics: identifying complex networks through relationships between accounts, devices, and behaviors.

These capabilities redefine the role of monitoring: from observing transactions to understanding behavioral ecosystems.

Juniper projects that global spending on AML solutions will increase from USD 33.9 billion in 2025 to USD 75 billion in 2030. Growth does not come only from new companies, but from greater technological density within each institution: more modules, more structured signals, more data integration, more real-time monitoring.

The metric is no longer which tool is used but how many layers an organization incorporates.

Some key data points from the study help illustrate this trend:

• 3.8 million companies are projected to integrate third-party AML solutions by 2030.
• Projected spending on AML systems is estimated at $75 Billion by 2030.
• The AML systems market is expected to grow by +121% between 2025 and 2030.
• Banks will account for 64% of global AML spending in 2030.
• Global sanctions for AML/CFT non-compliance totaled $4.6 Billion in 2024.
• U.S. authorities issued 95% of the global AML sanctions total in 2024.

• Prevention replaces late detection: Synthetic identities, digital mules, and deepfakes require controls from the first interaction; detecting them late leads to direct losses.
• The gap between institutions widens: Those relying on rule-based engines face more operational friction, false positives, and sanction risks.
• AML becomes a shared language: E-commerce, gaming, fintech, banking, and insurance are beginning to operate with common concepts: traceability, ownership, structured signals, continuous monitoring.

The global regulatory push and the rise of digital fraud are raising expectations for any company moving money in the region. The most visible effects will be:

• Stricter requirements for international operations: To move money with banks or platforms in other countries, companies must demonstrate stronger identity controls and traceability.
• Higher costs and operational load for fintechs: User onboarding and continuous monitoring processes will become more demanding, with more information requirements and more controls to comply with new rules.
• Need for reliable banking data: Companies will need access to accurate and standardized banking information to better understand risk and make fast decisions.
• Greater use of AI in monitoring: Teams will rely on systems that review user and transaction behavior to immediately identify suspicious signals.
• Higher exposure to digital fraud: Digitalization has expanded vulnerable points; preventing risk from the start will be key to avoiding losses.

These points suggest that the region will need to increase its prevention capacity if it wants to operate at the pace of the global digital ecosystem and maintain the trust of international partners.

In this context, having infrastructure that provides reliable banking data and structured signals—such as Prometeo’s bank account verification in LATAM—becomes essential to reduce friction and support risk decisions in onboarding and payments. This layer helps companies operate with greater accuracy before moving funds and comply with new standards requiring clear information from the start.

That more than 3.8 million companies will adopt AML systems by 2030 confirms a deep transition: compliance is no longer an isolated department but a critical infrastructure of the digital economy. The next decade will determine which organizations manage to integrate these layers coherently and which remain exposed in an environment where risk is no longer isolated but systemic.