AML Screening

The continuous, real-time analysis of transaction patterns to identify anomalies indicative of money laundering. Systems use rule-based engines and machine learning models to flag activities like:

• Structuring (breaking large sums into smaller transactions)
• Rapid movement of funds between accounts
• Transactions with high-risk jurisdictions Alerts are generated for further investigation by compliance officers.

The process of verifying a customer's identity and assessing their risk profile before and during the business relationship. This involves:

• Identity Verification (IDV): Collecting and verifying official documents.
• Risk Scoring: Assigning a risk level (e.g., low, medium, high) based on factors like occupation, geographic location, and transaction behavior.
• Ongoing Monitoring: Periodically updating customer information and risk assessments.

Automated checks against global watchlists to ensure a business is not transacting with prohibited entities. This includes screening for:

• Sanctions Lists: OFAC, UN, EU, and other government lists of restricted individuals, organizations, and countries.
• Politically Exposed Persons (PEPs): Individuals with prominent public functions who may pose a higher corruption risk.
• Adverse Media: Negative news related to financial crime. Matches generate false positive alerts that require manual review.

The mandatory process of filing a formal report to a national financial intelligence unit (e.g., FinCEN in the US) when a potentially illicit activity is detected. The report includes:

• Details of the suspicious transaction(s) and involved parties.
• The reason for suspicion.
• A narrative explaining the alert. Filing a SAR provides a safe harbor from liability for disclosing the activity.

A specialized screening method for cryptocurrency, using on-chain data to trace asset flows and cluster addresses. Key functions include:

• Address Clustering: Linking multiple addresses to a single entity or service (e.g., an exchange).
• Flow Analysis: Mapping the path of funds from origin to destination.
• Risk Attribution: Tagging addresses associated with mixers, darknet markets, or stolen funds. Tools like Chainalysis and Elliptic provide these services.

The core regulatory principle that mandates institutions to allocate compliance resources proportionally to the assessed risk. This involves:

• Tailored Controls: Applying enhanced due diligence (EDD) for high-risk customers and simplified measures for low-risk ones.
• Dynamic Adjustments: Continuously updating risk models based on new typologies and threats.
• Governance: Documenting the methodology for risk assessment and control selection, as required by regulators like the Financial Action Task Force (FATF).

The continuous, automated analysis of customer transactions to detect suspicious patterns indicative of money laundering or terrorist financing. Systems use rules-based algorithms and machine learning models to flag anomalies.

• Examples: Unusually large transfers, rapid movement of funds (structuring/smurfing), transactions with high-risk jurisdictions.
• Purpose: To identify and report suspicious activity as required by regulations like the Bank Secrecy Act.

The process of verifying a customer's identity and assessing their risk profile at onboarding and periodically thereafter. Know Your Customer (KYC) is a core component.

• Key Elements: Identity verification, beneficial ownership identification, understanding the nature of the customer's activities.
• Risk-Based Approach: Level of diligence (Simplified, Standard, Enhanced) is scaled according to the customer's risk rating.

Checking customers and their transactions against official government lists of restricted parties, such as sanctioned individuals, entities, or countries. This is a mandatory, real-time control.

• Lists Include: OFAC Specially Designated Nationals (SDN) list, EU consolidated list, UN Security Council sanctions.
• Matching Logic: Employs fuzzy matching and name-matching algorithms to account for spelling variations and aliases.

Also known as Negative News Screening, this involves scanning public sources (news, regulatory publications) for information linking a customer to financial crime or reputational risk.

• Purpose: To uncover risks not present on official sanctions lists, such as involvement in corruption, fraud, or organized crime.
• Method: Often uses Natural Language Processing (NLP) to automate the review of large volumes of unstructured data.

The identification of customers who are Politically Exposed Persons (PEPs) or their close associates, and those with ties to High-Risk Countries (RCAs). These relationships present a higher risk of bribery and corruption.

• PEPs: Individuals with prominent public functions, domestically or internationally.
• Enhanced Due Diligence (EDD): Mandatory for PEPs and customers from RCAs, involving deeper scrutiny of source of wealth and funds.

A specialized screening method for cryptocurrency and blockchain transactions. It analyzes the public ledger to trace fund flows and cluster addresses to real-world entities.

• Core Functions: Wallet screening (associating addresses with risky services), transaction graph analysis, and exposure assessment to sanctioned protocols or mixers.
• Tools: Used by VASPs (Virtual Asset Service Providers) to comply with the Travel Rule (FATF Recommendation 16).

Blockchain transactions occur between wallet addresses, not directly identifiable individuals, creating a fundamental tension with traditional Know Your Customer (KYC) requirements. While on-chain analytics tools can cluster addresses and trace fund flows, definitively linking an address to a real-world identity remains a significant challenge without off-chain data or voluntary disclosure.

Enforcing AML rules on decentralized applications (dApps) and automated market makers (AMMs) is complex as they are non-custodial and governed by code. There is no central entity to perform screening. Solutions involve integrating screening at the wallet or front-end interface level, or using privacy-preserving compliance tools that screen without exposing full user data.

DeFi protocols are globally accessible, but AML regulations vary significantly by jurisdiction (e.g., FATF Travel Rule, EU's MiCA). This creates compliance uncertainty for developers and users, who may be subject to conflicting rules. The lack of a unified global framework leads to a patchwork of requirements that are difficult to implement on a single, borderless protocol.

Traditional finance screens transactions before execution. In DeFi, smart contract transactions are often irreversible and settle in seconds, leaving minimal time for pre-execution checks. This necessitates the development of real-time risk scoring engines and on-chain monitoring oracles that can assess transactions against sanctions lists and risk indicators at the point of interaction.

Technologies like zero-knowledge proofs (ZKPs) and coin mixers enhance user privacy but can obscure transaction trails, making traditional AML screening impossible. This creates a technological arms race between privacy developers and compliance analysts, pushing for new regulatory-compliant privacy solutions that allow for proof of compliance without revealing underlying data.

The foundational process of verifying a client's identity before or during the establishment of a business relationship. It is a prerequisite for effective AML Screening.

• Core Components: Identity verification, document checks, and risk assessment.
• Relationship to AML: KYC provides the initial customer data that is then continuously screened against sanctions and watchlists.

Official registers published by governments and international bodies (e.g., OFAC, UN, EU) that identify individuals, entities, and countries subject to economic and trade restrictions.

• Primary Data Source: These lists are the core datasets used in transaction screening and customer due diligence.
• Dynamic Nature: Lists are frequently updated, requiring screening systems to have real-time or daily updates.

An individual entrusted with a prominent public function, who presents a higher risk for potential involvement in bribery or corruption due to their position and influence.

• Enhanced Due Diligence (EDD): Mandates stricter, ongoing monitoring of PEPs, their families, and close associates.
• Screening Target: PEP databases are a critical component of AML screening beyond basic sanctions lists.

The ongoing analysis of customer transactions to identify patterns of behavior that may indicate money laundering, terrorist financing, or other financial crime.

• Behavioral Analysis: Uses rules and machine learning to flag unusual activity (e.g., structuring, rapid movement of funds).
• Post-Screening Action: Often follows initial AML screening; a flagged transaction from monitoring may trigger a new sanctions/PEP check.

The process of scanning news sources, regulatory filings, and other public data for negative information about a client or potential client that indicates financial crime risk.

• Risk Indicator: Uncovers risks not yet formalized on official sanctions lists, such as ongoing investigations or allegations.
• Unstructured Data Challenge: Often involves analyzing qualitative data from diverse sources, increasingly aided by NLP (Natural Language Processing).

The global money laundering and terrorist financing watchdog. It sets international standards (the FATF Recommendations) that national AML/CFT laws are based upon.

• Regulatory Driver: FATF's guidelines define the requirements for Customer Due Diligence (CDD) and the scope of AML screening obligations.
• Mutual Evaluations: FATF assesses countries' compliance, creating pressure for robust AML regimes, including screening.