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AML customer types and how they shape AML risk

  • Writer: azakaw
    azakaw
  • Oct 1
  • 7 min read

Updated: Dec 18

AML customer types sit at the core of any risk-based compliance framework. Without accurate segmentation, institutions struggle to judge a customer’s financial crime exposure, and that means threats slip through unnoticed.


The problem? Regulators now expect firms to classify customers precisely and apply those classifications consistently. Getting it wrong is no longer a minor oversight; it directly weakens your ability to detect suspicious activity.


If you want to strengthen due diligence, improve monitoring accuracy, and stay aligned with global expectations, keep reading. This guide breaks down the AML customer types that matter most and how to use them effectively.



What are AML customer types?

AML customer types is a way of categorising clients based on their financial crime risk, typically into low, medium, or high-risk groups.


These categories reflect the likelihood that a customer may be involved in money laundering or terrorist financing and determine the level of due diligence required.


Factors such as occupation, legal form, beneficial ownership, jurisdiction, and the nature of expected activity help determine where a customer sits on the risk spectrum.


This approach enables institutions to tailor controls to each customer's actual profile, ensuring that higher-risk relationships receive closer oversight while lower-risk ones move through the process with proportional checks.


Why customer type matters in AML

Connection to customer due diligence (CDD)

CDD starts with understanding the customer type.


Retail individuals usually undergo standard checks, while high-risk customers, including offshore structures or individuals with political exposure. Usually, this process demands a far more detailed review of their wealth background, ownership arrangements, and any links that could indicate corruption.



Impact on risk scoring

Customer type determines how each customer is placed within the AML risk scoring. This influences inherent risk categories and the threshold for escalation.


In our experience, a cash-intensive business or a trust registered offshore will always present different risks to a domestic retail customer, regardless of their stated purpose.


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Regulatory expectations

Regulators globally expect firms to demonstrate a structured and transparent approach to customer classification.


FATF, FinCEN, the FCA, DFSA, MAS, and UAE authorities all highlight the need to proportionally segment customer types.


What are the types of AML customers?

The main types of AML customers include: retail individuals, corporate clients, politically exposed persons (PEPs), non-profit organisations, cash-intensive businesses, offshore entities and trusts, crypto and fintech clients, high-net-worth individuals (HNWIs), and correspondent banking relationships.


Each category carries a different level of money-laundering or terrorist-financing risk.


Individual retail clients

Retail clients typically represent lower or moderate AML risk.


Their financial activity is often predictable: salary payments, regular spending, occasional savings transfers, and routine bill payments.


Risk increases when customers have unclear income sources and make high-value transfers that are inconsistent with their profile.



Corporate clients

Corporate clients can range from low to high risk, depending on the complexity of their legal and ownership structures.


A transparent, single-jurisdiction company with clear beneficial ownership may pose a modest risk.


By contrast, multi-layered entities or those involving nominee directors, shell structures, or high-risk countries require much stricter EDD.


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KYB & Corporate Verification

Create a robust customer profile to tackle complex requirements, mitigate fraud risks, and uncover hidden ownership structures.




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Politically Exposed Persons (PEPs)

PEPs in AML represent a defined high-risk category due to their exposure to corruption, bribery, and misuse of public office.


FATF distinguishes between domestic PEPs, foreign PEPs, and senior officials of international organisations.


All require enhanced due diligence, but the degree of risk varies depending on jurisdiction and seniority.


Non-profit organisations and charities

NPOs are vulnerable to misuse for terrorist financing, particularly when operating internationally or relying on informal payment channels.


Risk assessment should focus on governance, funding transparency, and jurisdiction.


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Cash-intensive businesses

Restaurants, bars, gaming venues, small retailers, petrol stations, private transport operators, and similar businesses fall into a higher AML risk category because of the volume of physical cash handled.


These business types require robust scrutiny, including verification of cash-flow patterns and, in some cases, on-site inspections.


Offshore entities and trusts

Offshore companies and trusts are consistently classified as a high-risk AML customer type. Their complexity and opacity make it difficult to determine beneficial ownership, creating opportunities for layering and concealment.


Enhanced due diligence is mandatory for both onboarding and ongoing review, according to the DFSA AML Rulebook and the UAE Cabinet Decision No. 10 of 2019.


Many offshore structures also operate across jurisdictions with limited regulatory cooperation, which can slow information requests and make it harder for institutions to verify the legitimacy of assets or the entity's true purpose.


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Crypto customers and fintech platforms

Crypto exchanges, digital asset traders, and Web3 platforms are emerging customer segments with rapidly evolving high-risk profiles.


Their exposure to pseudonymity, decentralised networks, and cross-border transfers requires specialised AML controls and advanced transaction monitoring.


These customers may also operate at high transaction velocity, which increases the difficulty of identifying anomalous patterns in real time.


In addition, the frequent use of non-custodial wallets and mixing services can obscure the origins of funds, requiring compliance teams to employ far more sophisticated tracing techniques. 


In this context, the Travel Rule applies, requiring virtual asset service providers to collect, verify, and transmit identifying information for both the sender and the recipient whenever a crypto transfer occurs between regulated entities. It is enforced across all major financial jurisdictions.


High-net-worth individuals (HNWI)

HNWI clients hold complex assets and multiple accounts across different jurisdictions. While most are legitimate, their financial sophistication creates elevated AML exposure.


HNWIs routinely require detailed source-of-wealth assessments and ongoing behavioural monitoring to detect anomalies.


Their use of investment vehicles, family offices, and bespoke financial arrangements can also obscure transactional patterns, increasing the need for heightened oversight.


Correspondent banking clients

Correspondent banking sits at the upper end of AML risk because the relationship extends far beyond the immediate counterparty.


The bank providing the service is exposed not only to the correspondent itself but also to the breadth and quality of its underlying customer base, its controls, and its overall financial crime environment.


Weak governance or inadequate monitoring on the correspondent’s side can flow straight into the host institution’s systems, amplifying cross-border exposure in ways that are difficult to detect early.



How to assess customer types in AML programs

During onboarding

Customer classification begins during KYC onboarding, using identity data, corporate documents, beneficial ownership records, business activity, and AML screening.


The initial customer type establishes the foundation for CDD or EDD.


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Automated KYC verification

Comprehensive KYC verification combining identity checks, document validation, and risk scoring in one secure compliance platform.



Ongoing monitoring triggers

An AML customer type may change as new information emerges. Triggers include:

  • unusual transactions;

  • new jurisdictions;

  • updated ownership;

  • behaviour that no longer fits the expected profile.


This reinforces the importance of conducting regular updates to customers' data.


Risk rating adjustments over time

With dynamic risk scoring, customer classifications adjust automatically as new behavioural data enters the system and updates are expected by regulators, such as the FCA and ADGM for UAE institutions.


This allows the risk model to respond to emerging patterns and shifts in customer activity, ensuring AML controls remain aligned with actual risk rather than a static onboarding assessment. 


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AML Guidelines for categorising customers

FATF's risk-based approach

FATF establishes the global standards and requires institutions to adopt a risk-based approach, segmenting customers into AML risk categories and applying controls proportionate to their exposure.


Customer types must be clearly defined, justified, and documented.


Regional regulators

  • FinCEN emphasises transparency in beneficial ownership and identification of higher-risk entities. 

  • FCA guidance focuses on proportionality and defensibility of classification methods. 

  • DFSA requires detailed explanations for corporate and offshore structures. 

  • MAS highlights the use of technology, particularly in fintech and digital assets onboarding.


Alignment with customer due diligence levels

AML customer types must directly map to CDD and EDD levels. Low-risk individuals receive simplified checks while high-risk corporate or offshore entities require enhanced, ongoing assessment.


Technology and automation in risk classification

Role of AML software in customer risk rating

Modern AML software uses rule-based engines and machine learning to automatically classify customers.


Specialised software improves accuracy, reduces manual workload, and standardises risk scoring.


Integration with KYC and transaction monitoring tools

Customer type information must flow seamlessly between onboarding, transaction monitoring, and suspicious activity systems. This integration ensures alerts reflect the actual risk of the customer profile.


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AML Transaction tool

Monitor transactions in real time with automated AML checks, risk scoring, and alerts that help you detect suspicious activity instantly.



Benefits of dynamic risk scoring engines

Dynamic engines allow real-time risk updates based on behaviour, geographic exposure, ownership changes, and transactional activity. These engines prevent outdated classifications and strengthen early warning detection.


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FAQs

Can a customer have more than one AML customer type?

Yes. For example, an individual may be both a personal retail client and the beneficial owner of a corporate entity.


How do customer types affect AML risk scoring?

Customer type determines inherent risk levels and shapes the required intensity of due diligence. High-risk customer types start with elevated scores and undergo enhanced monitoring.



Are PEPs always high risk?

It depends. Domestic PEPs in low-risk jurisdictions present a different risk than foreign PEPs with high corruption exposure, but all PEPs require EDD.


Can the customer's risk level change over time?

Yes. Risk levels evolve as customers transact, move between jurisdictions, restructure ownership, or change business models.


What tools help classify customer types in AML?

Specialised AML software, KYC onboarding platforms, transaction monitoring engines, and dynamic risk scorers all support accurate and ongoing classification.


Conclusion

AML customer types form the structural core of any risk-based AML programme. They determine how CDD is applied, how risk scoring is calculated, and how effectively institutions detect suspicious activity.


As financial ecosystems become more complex, automated classification and dynamic scoring are essential for maintaining regulatory compliance and operational efficiency.


Discover how Azakaw automates customer risk classification and strengthens end-to-end AML workflows.


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Sources

 
 
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