FATF ¦ R.33 Sta­tis­tics

Recommendation 33: Why Strong AML/CFT Statistics are a Strategic Necessity

The fight against money laundering and terrorist financing is only as strong as the information that guides it. Recommendation 33 focuses on one of the most practical but often underestimated foundations of an effective AML/CFT framework: comprehensive statistics. Without reliable data, policymakers and operational agencies are essentially working in the dark, unable to measure whether their systems actually work or where they need to improve.

Understanding the Core of Recommendation 33

Recommendation 33 requires countries to maintain comprehensive statistics on all key aspects related to the effectiveness and efficiency of their AML/CFT systems. This is not a mere administrative exercise. It is about giving governments, regulators, and law enforcement a factual basis to:

• assess what is working;
• identify gaps and weaknesses;
• allocate resources intelligently;
• demonstrate effectiveness to international partners and standard setters.

At its core, Recommendation 33 is about evidence-based AML/CFT policy and operations. It pushes countries to move beyond checking boxes in laws and regulations and start proving, with data, that their systems deliver real outcomes.

Why Comprehensive Statistics Matter in AML/CFT

Money laundering and terrorist financing are adaptive, cross-border, and constantly changing. Static laws do not keep up with dynamic threats. Data becomes the only reliable method to track that evolution. Good statistics help answer crucial questions, such as:

• Are suspicious transaction reporting systems generating useful leads?
• Are financial crime investigations resulting in prosecutions and convictions, or do cases stall?
• Are asset freezing and confiscation tools actually depriving criminals of their profits?
• Is international cooperation functioning efficiently, or are requests stuck in bureaucratic bottlenecks?

Without statistics, each of these questions is answered with guesswork, anecdotes, or isolated success stories. With statistics, countries can measure trends over time, identify bottlenecks, and adjust their approach. Data also allows comparison: between sectors, between agencies, and even between countries.

Bastian Schwind-Wagner _Follow

"Recommendation 33 underlines that effective AML/CFT frameworks depend on solid, comprehensive statistics rather than assumptions or isolated success stories. Countries need reliable data on STRs, investigations, prosecutions, convictions, asset measures, and international cooperation to understand whether their systems genuinely work. Without this, risk-based supervision and policy-making quickly lose precision and credibility.

For financial crime professionals, this focus on measurable outcomes means that “showing the numbers” is becoming just as important as having policies on paper. Quality, not just quantity, will drive expectations around STRs, enforcement outcomes, and asset recovery. In practice, Recommendation 33 is a reminder that you cannot manage financial crime risks if you are not systematically measuring them."

Key Categories of Statistics Required Under Recommendation 33

The standard highlights several categories of statistics that countries should maintain as part of a robust AML/CFT framework. Each of these categories helps to capture a different part of the system’s performance.

1. Suspicious Transaction Reports (STRs) Received and Disseminated

The first set of statistics relates to STRs, which sit at the center of most AML systems. Countries should record:

• How many STRs are received by the financial intelligence unit (FIU).
• The types of reporting entities filing them.
• How many STRs are analyzed and result in disseminations to law enforcement or other competent authorities.

The volume of STRs alone does not indicate effectiveness. A very high number can signal over-reporting or defensive reporting. A very low number may indicate under-reporting or lack of awareness. Only by tracking STR statistics over time and linking them to outcomes (like investigations and convictions) can a country understand whether its reporting regime is producing quality intelligence.

2. Money Laundering and Terrorist Financing Investigations, Prosecutions, and Convictions

A central purpose of the AML/CFT framework is to bring actual cases of money laundering and terrorist financing to justice. Recommendation 33 therefore requires statistics on:

• the number of investigations initiated;
• the number of prosecutions brought before the courts;
• the number of convictions obtained, including details on sanctions.

These figures help answer whether the system is capable of effectively enforcing the law. For instance, a country with many STRs and many investigations but very few prosecutions or convictions may face problems such as weak legal frameworks, insufficient evidence gathering, lack of specialization in financial crime, or procedural barriers.

Separating statistics between money laundering and terrorist financing is also important because the nature, scale, and priority of these threats can differ significantly. Detailed statistics support tailoring policy responses to the specific risks of each.

3. Property Frozen, Seized, and Confiscated

Following the money is not just about punishing offenders; it is about removing the financial gains that motivate crime. Recommendation 33 calls for statistics on:

• the value and number of assets frozen (often at an early stage);
• the value and number of assets seized during investigations;
• the value and number of assets ultimately confiscated by final court decisions.

Monitoring these figures helps answer several important questions:

• Is the country effectively identifying and tracing criminal proceeds?
• Are legal tools for seizure and confiscation being used in practice?
• Are there procedural or institutional obstacles between freezing, seizing, and final confiscation?

A system where many assets are frozen but very few are eventually confiscated may indicate that provisional measures are used but rarely translated into final outcomes. This can point to weak case building, legal challenges, or ineffective asset management and recovery frameworks.

4. Mutual Legal Assistance and Other International Cooperation Requests

Financial crime is rarely confined within one jurisdiction. Effective AML/CFT systems depend heavily on international cooperation. Recommendation 33 therefore requires statistics on:

• Requests for mutual legal assistance (MLA) received and sent.
• Other international cooperation requests, such as information exchanges between FIUs or supervisory authorities.
• How many of these requests are executed, refused, or remain pending.
• The time taken to respond to such requests.

These statistics reveal how well a country functions as an international partner. Long delays or low execution rates can damage trust and hinder the investigation of cross-border cases. They also highlight where investment is needed, whether in training, staffing, or streamlining legal procedures for cooperation.

Using Statistics to Assess Effectiveness, Not Just Compliance

Recommendation 33 is closely linked to the broader shift in AML/CFT assessments from “technical compliance” (laws on the books) to “effectiveness” (what happens in practice). For financial crime professionals, the message is clear:

• Having laws and institutions is not enough; you must show results.
• Those results must be backed by credible, systematic data.

Countries that treat statistics as a formality risk failing to detect structural problems, such as:

• a reporting framework generating large quantities of low-value STRs;
• law enforcement overwhelmed with financial data but unable to convert it into strong cases;
• asset recovery measures that look strong in law but weak in practice;
• cooperation mechanisms that exist but are practically ineffective.

By contrast, countries that invest in quality statistics can fine-tune their AML/CFT frameworks, focus resources on high-risk areas, and demonstrate to external evaluators that they understand and manage their risks.

Practical Challenges and Considerations for Countries

Implementing Recommendation 33 is not just about keeping numbers. Countries face several practical challenges:

• Data fragmentation: Different agencies (FIUs, law enforcement, prosecutors, courts, supervisors, asset management offices) may hold different pieces of the puzzle. Consolidating consistent statistics requires coordination and common definitions.
• Data quality and consistency: If concepts like “investigation”, “prosecution”, or “confiscation” are defined differently across institutions, national statistics can become unreliable or misleading. Clear definitions and standard reporting formats are essential.
• IT systems and infrastructure: Many jurisdictions still rely on outdated systems or manual processes. To maintain comprehensive and timely statistics, countries often need integrated databases and case management tools.
• Confidentiality and data protection: Handling sensitive financial and criminal justice data requires careful balancing between transparency, operational security, and privacy laws.

Addressing these challenges requires political commitment, clear mandates, and cooperation between ministries, supervisors, FIUs, and law enforcement bodies.

Implications for Financial Institutions and Compliance Teams

While Recommendation 33 is directed at countries, it has indirect but significant consequences for financial institutions and other reporting entities:

• STR expectations: If authorities are measuring STRs and their outcomes, they will be more focused on quality rather than just quantity. Institutions can expect closer scrutiny of whether their internal systems detect relevant, risk-based suspicious activity.
• Feedback loops: Robust national statistics often lead to better feedback from authorities, such as typologies, red flags, and sector-specific risk insights. This improves the quality of internal controls and helps institutions align with actual national risks.
• Regulatory focus: Data revealing weak enforcement or low confiscation rates may push authorities toward more aggressive supervision and enforcement in high-risk sectors or products.

For compliance officers, understanding Recommendation 33 helps interpret why regulators request certain data, how they measure performance, and where regulatory focus is likely to intensify.

Conclusion: Statistics as the Backbone of an Effective AML/CFT System

Recommendation 33 places statistics at the center of a modern, risk-based, and outcome-focused approach to fighting financial crime. Comprehensive data on STRs, investigations, prosecutions, convictions, asset measures, and international cooperation is not a bureaucratic burden; it is a strategic necessity.

For countries, implementing this recommendation means building a data-driven AML/CFT framework that can be evaluated, improved, and trusted at both domestic and international levels. For financial institutions and professionals, it signals a continued shift toward measurable effectiveness, where the quality of reporting, cooperation, and controls will be scrutinized through hard numbers, not just written policies.

In short, Recommendation 33 reminds everyone involved in financial crime prevention that you cannot manage what you do not measure — and in AML/CFT, failing to measure means failing to keep up with the threat.

Dive deeper

• FATF ¦ The FATF Recommendations ¦ Link
• FATF ¦ Luxembourg’s measures to combat money laundering and terrorist financing ¦ Link