Quick Answer: What is the money laundering act? #
Money laundering acts are laws and regulations designed to detect, prevent, and penalize the process of disguising illicit funds as legitimate income. Their primary goal is to make it harder for criminal enterprises to move and integrate illicit money into the financial system undetected.
Without strong anti-money laundering (AML) frameworks, financial institutions risk facilitating crime, facing massive fines, losing their banking licenses, and eroding public trust.
To comply with money laundering acts, institutions must:
- Establish a risk-based AML program
- Monitor transactions and flag suspicious activity
- Verify customer identities through KYC processes
- Maintain detailed records for audits and investigations
- Report suspicious transactions to financial intelligence units (e.g., FinCEN in the U.S.)
- Train staff on AML procedures and legal obligations
Up next, we’ll break down the most important AML acts around the world, common compliance challenges, and how modern data platforms can help enforce AML rules in real time.
Table of contents #
- Money laundering act explained
- Why do money laundering acts matter?
- From regulation to implementation: Turning AML policy into data requirements
- What are the challenges in ensuring compliance with anti-money laundering acts?
- What role does active metadata play in complying with money laundering acts?
- How can a metadata control plane help with AML compliance?
- Money laundering act: Final thoughts
- Money laundering act: Frequently asked questions (FAQs)
- Money laundering act: Related reads
Money laundering act explained #
Money Laundering Acts are laws designed to detect and prevent illicit money flowing through financial systems. These laws obligate banks, insurers, fintechs, and other financial institutions to monitor financial activity, flag suspicious transactions, and maintain clear audit trails.
Anti-money laundering (AML) acts introduce non-negotiable requirements for customer due diligence, transaction monitoring, reporting, and data retention. AML non-compliance can lead to regulatory penalties, loss of operating licenses, and reputational damage.
For instance, in 2024, Metro Bank was fined £10 million by the UK’s Financial Conduct Authority (FCA) for failing to maintain proper financial crime controls. The bank’s system didn’t flag suspicious activity effectively largely due to weak data and poor monitoring frameworks.
Why do money laundering acts matter? #
AML acts are more important than ever because, as McKinsey highlights, the growth in volume of cross-border transactions and greater integration of the world’s economies have made banks inherently more vulnerable.
The U.S. anti-money laundering (AML) framework has steadily evolved from tracking paper trails to verifying people and their intentions. And every shift in regulation brings a shift in how data must be collected, shared, and governed.
Let’s look at some of the most significant developments in the U.S. AML laws.
From paper trails to people: How U.S. AML laws evolved toward identity-based enforcement #
Bank Secrecy Act or BSA (1970): Start of reporting
- Initially there was little to no tracking of large cash movements and transactions. Even suspicious ones could flow freely through the system without raising a red flag.
- The BSA aimed to create visibility by requiring banks to report transactions over $10,000 (daily aggregate amount).
- While this gave regulators more insight, there were no real consequences for money laundering itself.
Money Laundering Control Act (1986): Money laundering becomes a crime
- The Money Laundering Control Act criminalized the act itself.
- This act also increased the criminal penalties for BSA violations.
Anti-Drug Abuse Act (1988): Identity checks begin
- This is where the shift began — from tracking just the transaction to looking at the person behind it.
- The Anti-Drug Abuse Act required ID verification for anyone buying monetary instruments over $3,000.
- Brought non-bank entities under reporting rules, extending AML obligations beyond just financial institutions.
Annunzio-Wylie Act (1992): SARs introduced
- After the BCCI scandal, the Annunzio-Wylie Act introduced Suspicious Activity Reports (SARs).
- The introduction of SARs required data teams to focus on identifying suspicious patterns, not just on large transactions. This meant building systems that could flag unusual behavior and integrate real-time anomaly detection into compliance.
- A case related to SARs involved a used car dealer accepting cash from people tied to drug trafficking. He structured deposits keeping each just under $10,000 to avoid reporting rules, then wired the money overseas. A bank noticed the unusual pattern and filed a SAR. That report triggered an undercover investigation, and the dealer later pleaded guilty to laundering $35,000.
Money Laundering Suppression Act (1994): MSBs regulated
- Prior to the Money Laundering Suppression Act, check cashers, wire transfer services, and currency exchanges weren’t regulated like banks, which made them easy targets for money laundering.
- The Money Laundering Suppression Act (1994) addressed this gap by bringing Money Services Businesses (MSBs) under the same AML rules as banks.
- Money Services Businesses had to start registering and following AML procedures strictly.
The Patriot Act (2001): AML meets national security
- The Patriot Act changed the landscape overnight. It tied AML to national security.
- It required enhanced due diligence (EDD) on foreign/private accounts and boosted data sharing between banks and the government.
FinCEN’s CDD Rule (2016): Transparency push
- FinCEN’s CDD Rule requires firms to collect data on who really owns companies.
- Banks had to identify and verify the beneficial owners behind legal entities — not just shell companies on paper.
- For data teams, this means new demands around entity resolution, ownership hierarchies, and stricter KYC data integration.
The AML Act of 2020 (2020): AML enters the digital age
- The AML Act of 2020 brought AML into the fintech and crypto era. It addresses gaps in crypto, shell entities, and cross-agency data sharing.
- Key changes include:
- Required reporting on digital assets
- Create a national beneficial ownership database
- Emphasis on tech with real-time monitoring, APIs, cross-system coordination
While the U.S. was evolving its AML laws around identity and behavior, the EU was building its own framework through a series of directives aimed at closing systemic gaps. Let’s take a look.
EU anti-money laundering directives: An overview #
- Scope: Applied to financial institutions.
- Requirements: Introduce mandatory customer due diligence (CDD), record-keeping, and reporting of suspicious transactions.
- Scope expansion: Extended AML obligations to non-financial sectors, including lawyers, accountants, and real estate agents.
- Purpose: Aimed at closing gaps exploited by money launderers outside traditional banking.
- Risk-based approach: Introduced a risk-based approach to AML, requiring enhanced due diligence for high-risk customers.
- PEPs: Mandatory identification and monitoring of Politically Exposed Persons (PEPs).
- Beneficial ownership: Required member states to maintain central registers of beneficial ownership information for legal entities.
- Enhanced CDD: Strengthened CDD measures and expanded the definition of obliged entities.
- Virtual currencies: Bring virtual currency exchange platforms and wallet providers under AML regulation.
- Transparency: Increased transparency of beneficial ownership registers and reduced anonymity in financial transactions.
- Harmonization: Standardized the definition of money laundering offenses across the EU.
- Liability: Introduced criminal liability for legal persons and extended the list of predicate offenses, including cybercrime and environmental crime.
From regulation to implementation: Turning AML policy into data requirements #
Here’s a breakdown of what the law demands — and the systems organizations need to implement to meet those demands without turning compliance into chaos.
What the law requires | What data system must do |
|---|---|
Know Your Customer (KYC/CDD) | Build a single source of truth for customer identity by stitching together siloed customer data across systems. |
Track beneficial ownership (UBO) | Map ownership hierarchies across people and companies using active metadata, lineage, and entity resolution. |
File Suspicious Activity Reports (SARs) | Maintain audit-grade lineage and access logs to track who did what, when — across tools, pipelines, and teams. |
Spot suspicious patterns | Apply consistent risk logic across systems with policy-as-code, reusable templates, and embedded governance. |
Real-time monitoring | Connect tools, APIs, and data flows across systems for real-time monitoring, anomaly detection, and alerts. |
What are the challenges in ensuring compliance with anti-money laundering acts? #
Most financial institutions continue to face challenges in ensuring AML compliance because of:
- Poor data quality: Many institutions operate with bad quality data. According to Thomson Reuters, flawed data quality can undermine the entire AML pipeline. Bad source data means bad alerts, false positives, and missed red flags. It also creates compliance blind spots and leaves institutions exposed to audit failures and regulatory penalties.
- Legacy systems and manual processes: Legacy systems make it difficult to aggregate customer data across legal entities, subsidiaries, and vendors. McKinsey points out that compliance teams resort to costly workarounds—like making thousands of monthly customer calls just to refresh outdated or missing KYC data.
- Inconsistent compliance processes and standards: Key processes like customer identification, enhanced due diligence, and account monitoring are often handled differently across departments or regions. Without unified standards, teams lack clarity on what constitutes risk, leading to both under- and over-reporting.
- Regulatory complexity: In an interview by the Dutch Association of Insurers (Verbond van Verzekeraars) Melissa van den Broek, Senior Manager at KPMG Forensic Integrity & Compliance, noted that “European anti-money laundering rules will become more extensive, more complex and more detailed.” Every year brings new reporting requirements, tighter KYC/CTF rules, and increased expectations for traceability and auditability. Regulators now want clear logs, traceability, and proof, making AML compliance tougher.
- Inadequate suspicious activity reporting (SAR/STR): Inconsistencies in how and when SARs or currency transaction reports are filed result in either regulatory scrutiny for underreporting—or unnecessary expense and burden from overreporting. Institutions walk a narrow line between non-compliance and overkill.
- Manual workflows and limited automation: Many compliance teams are stuck doing “stare and compare” reviews instead of meaningful investigations. Fragmented systems limit automation of transaction monitoring and due diligence, forcing teams to spend valuable time collecting and reconciling data manually.
- Lack of risk visibility across the enterprise: Reliable, quantitative metrics to assess AML risk across products, geographies, and channels are often missing. This makes it harder to prioritize high-risk areas and align controls with actual exposure.
- Rapid product evolution adds pressure: The constant rollout of new offerings—instant payments, mobile wallets, even smart ATMs—introduces complex new risk vectors. Many of these innovations lack adequate safeguards for KYC or transaction monitoring, making real-time AML enforcement significantly harder.
Together, these challenges highlight why AML compliance demands high-quality, connected data, consistent and real-time monitoring, and smart automation driven by metadata-rich systems.
What role does active metadata play in complying with money laundering acts? #
AML compliance today is a data and metadata problem. That’s where active metadata—metadata that continuously updates and flows across systems—becomes critical. It is the connective tissue that brings everything together.
Here’s how it helps:
1. Build a unified view of customers and entities: Active metadata helps consolidate siloed data into a single, auditable view of a customer or legal entity. This is essential for accurate KYC/CDD, beneficial ownership tracking, and resolving complex ownership hierarchies across shell companies and affiliates.
2. Enable real-time monitoring and alerts: Metadata-powered automated data lineage and pipeline monitoring can trigger real-time alerts when risky data behavior occurs, such as large fund transfers, sudden changes in customer behavior, or bypassing of known AML checks.
3. Standardize and enforce policies across tools: AML rules like threshold alerts, PEP screening, or transaction blocking can be defined as metadata-driven policies and applied uniformly across tools (e.g., Snowflake, Databricks, BI dashboards, ETL pipelines). No more scattered rule logic or inconsistent enforcement.
4. Automate documentation and audit trails: Active metadata captures who accessed what, when, and how. This lineage information helps generate audit-ready documentation, track suspicious activity, and prove regulatory compliance during investigations.
5. Reduce false positives with better context: By embedding metadata into models and scoring logic, financial institutions can include contextual information—like transaction lineage, geography, channel, and behavior patterns—resulting in smarter risk scoring and fewer false positives.
6. Simplify regulatory change management: When AML rules evolve, metadata platforms help trace impact. You can assess which data assets, teams, or reports are affected, making it easier to update systems without breaking compliance.
Scaling active metadata management across your data estate requires a unified metadata control plane like Atlan.
How can a metadata control plane help with AML compliance? #
A metadata control plane like Atlan enables AML teams to:
1. Unify fragmented data sources for KYC/CDD by automatically connecting metadata across systems like Snowflake, BigQuery, dbt, and internal KYC platforms
2. Track suspicious behavior with end-to-end lineage, making it easy to trace suspicious activity from reports, dashboards, or alerts back to the source systems and transformations
3. Define AML policies as metadata tags and apply them automatically across the entire data stack
4. Detect and prevent AML violations in real time, so teams can spot anomalies, assess risk, and take action right away
5. Simplify audit preparation and regulatory reporting with AI-powered documentation, metadata versioning, and more
6. Align teams with a single source of truth, centralizing definitions, tags, and ownership
In short, Atlan turns active metadata into an operational foundation for AML compliance, helping data and compliance teams detect risk sooner, enforce policy everywhere, and respond to audits or investigations with speed and clarity.
Case in point: Austin Capital Bank #
Austin Capital Bank, a fast-growing community bank, improved its data setup to support fast innovation and meet regulatory needs. In just 16 months, they built a modern data stack using Snowflake, Fivetran, dbt, ThoughtSpot, and Atlan for active metadata management.
With Atlan, the bank:
- Gained insight into data lineage and impact across tools.
- Built a shared glossary to make data definitions clearer.
- Set up access controls and data masking to protect customer information.
This solid foundation helped the bank streamline compliance, and launch new products.
Money laundering act: Final thoughts #
In this article, we saw how AML has evolved from simple reporting rules to complex, identity-driven enforcement. We explored how poor data governance undermines compliance efforts. Solving today’s AML challenges means treating compliance as a data governance problem, and that begins with building a unified, intelligent data foundation.
Money laundering act: Frequently asked questions (FAQs) #
1. What is the primary purpose of money laundering acts? #
Money laundering acts are designed to prevent criminals from disguising illicit funds as legitimate income. They establish legal obligations for financial institutions to detect, report, and prevent suspicious financial activity.
2. Which U.S. laws govern anti-money laundering (AML) compliance? #
Key U.S. AML laws include the Bank Secrecy Act (1970), Money Laundering Control Act (1986), USA PATRIOT Act (2001), FinCEN’s CDD Rule (2016), and the AML Act of 2020. These laws mandate reporting, customer due diligence, beneficial ownership checks, and transaction monitoring.
3. What is KYC, and how is it related to AML? #
Know Your Customer (KYC) refers to the process of verifying a customer’s identity and assessing risk before providing services. It’s a critical component of AML compliance, helping institutions detect and prevent money laundering and fraud.
4. What are the penalties for non-compliance with AML regulations? #
Penalties can include multi-million dollar fines, legal action, revocation of licenses, and severe reputational damage. For example, in 2024 Metro Bank was fined £10 million for AML control failures.
5. What’s the biggest data challenge in AML compliance today? #
Fragmented, poor-quality data. Most AML systems still rely on siloed sources and outdated ETL pipelines, making it hard to trace suspicious activity across systems.
6. How do modern AML regulations affect data architecture? #
Laws like the U.S. AMLA 2020 and EU AMLD6 now require proactive monitoring and identity resolution—forcing organizations to build architectures that can correlate structured and unstructured data in near real time.
7. How can metadata help with AML compliance? #
Metadata provides critical context—such as where data came from, how it’s used, and who accessed it. This visibility is essential for tracking transactions, resolving entities, identifying risk patterns, and creating audit-ready reports.
8. What kind of data systems are best suited for AML compliance? #
Systems that support active metadata, column-level lineage, and automated data classification —ideally with a shared control plane across teams.
9. What is a metadata control plane, and why does it matter for AML? #
A metadata control plane like Atlan centralizes metadata from across your data stack, making it easier to enforce policies, automate monitoring, and maintain consistent, real-time visibility into data access and usage. This is especially valuable for meeting AML requirements efficiently and at scale.