What Is a Risk-Based Approach to AML?

The 'risk-based approach' to AML was first suggested by the Financial Conduct Authority (FCA) in 2000 and was later amplified by the Financial Action Task Force (FATF) in 2012. 

A risk-based approach means that countries, competent authorities, and banks identify, assess, and understand the money laundering and terrorist financing risk to which they are exposed and take the appropriate mitigation measures per the level of risk as defined by FATF. 

This proactive management of risk provides a more practical stance for combating the flow of illicit money instead of waiting until these illegal transactions occur. It enables allocating resources and efforts based on the identified risks.

In this article, you will learn about a risk-based approach to AML and how to implement your business’s AML compliance solution. 

How to implement a risk-based approach to AML

Implementing a risk-based approach to AML is an ongoing process. The organisation needs to adapt to changing risks and regulations because they will be integrated into the institution's overall AML compliance culture. 

However, there are a few steps you can take to effectively implement an RBA in your AML processes,

Step 1: Getting the Right Information

This first step is about identifying the specific risks to your business. To effectively use the RBA, you need accurate and up-to-date information about money laundering (ML) and terrorist financing (TF) risks.

There are a few main types of risks you should consider in your research: 

Geographic risks: A proactive risk-based approach to AML must also factor in the market-specific or location risk present for a product or service. Some countries or regions may have a reputation for having weak or ineffective AML/CFT regulations and enforcement. These jurisdictions are often considered high-risk.

The risk-based approach to AML would factor in your business environment's laws, regulations, security, data privacy, and accuracy.

Channel risks: Channel risks revolve around the pattern through which your services are delivered to the customer and the inherent risk of money laundering that could be involved. 

For example, if you operate a third-party or payment service, your organisation is internet-mediated, with a higher risk of identity fraud. So, part of the risk-based approach to AML would be a robust digital identity verification and authentication process. 

Transaction risks: Complex transactions or ones involving certain payment types, such as cash or cryptocurrency, can also be assessed as a higher risk when building up AML solutions for your business.

A solid risk-based approach to AML for businesses would limit customers' transactions to flag off suspicious activities easily. 

A substantial transaction or unusual activity outside normal commercial activities may represent a risk factor. 

Customer risks: This is about identifying the types of customers you are targeting and their risk level. Are they generally higher-risk individuals like high net-worth individuals? Where are they located? 

Step 2: Assessing the Risks 

Once you know the risks, determine how they might affect your bank or the broader financial sector. This step is more than just gathering numbers and facts. It's about making sense of the information and keeping it fresh to stay relevant.

Sort these risks into categories like low, medium, or high. This helps prioritise and plan how much resources to allocate to mitigation. 

A helpful way to visualise the risk categorisation is to use a table for classification.

Step 3: Deciding on Action

Knowing the risk level lets you decide how to deal with it. High-risk situations need stronger actions. For lower risks, use more straightforward methods. Sometimes, you might even exempt specific sectors or activities from strict AML rules if you can show that their risk is low. 

It's about finding the right balance based on the situation's risk.

Common mitigation strategies include performing Know Your Customer checks on all customers, affiliated businesses, and suppliers. KYC is a set of processes that involves collecting and verifying customer or ultimate beneficial owner (UBO) information, such as their identity and address.

What are the challenges of a risk-based approach to AML?

A risk-based approach to AML can be effective. However, it also comes with some challenges, especially in markets with weak data infrastructure and frequently changing regulations.

• Data quality and availability: Financial institutions need access to high-quality data to assess risks effectively. They must collect, analyse, and update customer data, transactions, and other relevant information. Insufficient or inaccurate data can hinder the ability to assess and manage risks.

• Risk assessment accuracy: The risk of customers and transactions can be complex, and there is always a risk of misjudging or underestimating the actual risk. This can lead to compliance issues or even regulatory penalties.

• Local regulatory expectations: Regulatory expectations regarding risk assessment can vary from one jurisdiction to another. Keeping up with changing regulations and ensuring compliance across different markets can be challenging, especially for multinational businesses.

Keeping Financial Inclusion in Mind

While implementing RBA, it's essential to consider its impact on financial inclusion. Studies have shown that AML regulations, while necessary for financial system integrity, can sometimes hinder the financial inclusion of low-income people. 

An effective RBA should promote financial inclusion by providing tailored due diligence measures, especially in regions where regulations such as know-your-customer rules may inadvertently limit access to financial services.

In summary

The reason behind implementing a risk-based approach to AML is that it’s a proactive rather than reactive way to defend your organisation against money launderers. 

The RBA requires financial institutions to assess the risk level of their customers. Smile ID can contribute to this process by ensuring that the identity information provided is authentic, which is crucial for accurate risk profiling. 

By integrating biometric data, AML watchlist checks and fraud signals, Smile ID can help categorise customers based on risk levels, enabling institutions to apply the necessary AML measures more effectively.