AML Models – Definition, Development, Validation and Best Practices

Anti-money laundering (AML) practices help fight financial crimes by making it difficult to “wash” illegally acquired funds so that they look legitimate. Businesses, especially financial institutions and high-cash concerns, must be vigilant to prevent this criminal activity.

While AML compliance is a high priority, members of the industry are not in complete agreement about what qualifies as a model and how to stay AML compliant. Some companies offer “no-code” systems while others offer one or more components but not a true model.

Several key regulatory agencies are involved in monitoring the fulfillment of AML regulatory requirement compliance. In the U.S., for example, the Office of the Comptroller of the Currency (OCC) and the Financial Crimes Enforcement Network (FinCEN) provide updated regulatory guidance on the Bank Secrecy Act (BSA).

What Is an AML Model?

An AML model is a process, not a single compliance tool. An effective model uses expert human resources, advanced software and in-house financial rules to achieve governmental compliance.

Successful AML models rely on a mix of human expertise and some sort of artificial intelligence, including machine learning. You need a quantitative process that gathers and analyzes key data to assess AML risk. Much of your data will be gathered via computer, but your staff can also gather needed information through client interviews and surveys. Then you need sound algorithms that analyze the data and allow you to spot money laundering actions on your company’s accounts.

To make things more difficult, you need a model that can spot sophisticated financial malfeasance that does not burden you with excessive false alarms. Incorrect transaction flags cause you to lose productivity and faith in your AML models.

Types of AML Methods

Single AML methods offer a level of protection, but superior protection requires developing models based on best practices in the following areas.

Know Your Customer (KYC)

The investment and financial services industry has established guidelines known as KYC that require companies to verify their clients’ identities and financial profiles. FinCEN established minimum standards of KYC to stop bad actors from making illegal financial transactions. Every broker or financial services company must make reasonable efforts to weed out money laundering or terrorist financing by developing a customer risk rating for pursuing those activities.

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Transaction Monitoring

Financial institutions and other companies can reduce money laundering by practicing due diligence. Banks are required to report transactions of $10,000 or more, but they should also watch for other suspicious activity and trace the origin of large deposits. Transaction monitoring software can continually scan transactions and minimize false positives through machine learning.

Individual Company Protocols

Mandatory regulations are in place for AML, but each institution needs to craft its response in addition to those regulations. Increased holding periods on deposits, better investigative practices and increased employee awareness can help control money laundering transactions.

AML Model Development

Developing an effective AML model requires thoughtful analysis of various factors, including the use of advanced technology and educated human interventions. To develop your model, consider the following:

Determine a Clear Purpose for the Model

Develop a clear and achievable purpose for your model. You cannot eliminate money laundering, but you can set a goal to reduce it through meaningful action. You may target an area of criminal activity while reducing false positives. Know what you want to accomplish.

Develop a Robust Methodology

A robust methodology in any field means one that is accurate even when slight variations occur. You need to develop an accurate system based on the latest technology, risk assessment and other best practices that deliver reliable results.

Establish Model Controls

Your model must have internal controls that are appropriate to your industry and that are followed in every instance. If you do not put clear AML guidelines and processes in place, your results will be unverifiable.

Ensure That the Collected Data Is Accurate and Relevant

Data collection should be limited to pertinent areas. Too often, your data sets become too big to control or properly analyze. Make certain you get accurate, quality data only.

Run Tests Before Implementation to Ensure the Model Works as Intended

Test the system repeatedly with model inputs before implementing it so you know your model fulfills your purpose. Avoid launching it before you feel confident in its accuracy.

Implement Ongoing Monitoring Processes

Even the best-designed systems need to be monitored regularly. Before implementing your model, you should have your monitoring process in place.

Know the Limits of the Model

Your testing should reveal the limits of your model. Every AML model will have its weaknesses. Being aware of those weaknesses allows you to mitigate them.

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AML Model Risk Management Guidance

AML compliance is not something to take lightly. Your AML program should ensure regulatory compliance while minimizing the number of false positives. Your institution’s financial health and the well-being of your clients depend on it.

Proper testing and ongoing monitoring are key to appropriate AML model risk management. Failure of financial institutions to follow state and federal regulations can lead to costly fines or even incarceration.

Your model governance should include testing and other in-house validation processes but also the opinion of an expert or experts from outside your organization.

AML Model Validation

Your AML compliance program is only as good as your validation process. Creating a faulty model can create an entirely new set of problems. To make certain your model is sound, you need to consider the following:

Conceptual Soundness

Conceptual soundness is fundamental to all risk management programs. You need to verify that your model design is backed up with documentation and empirical evidence of its effectiveness and of the variables you used for the model.

Input and Data Evaluation

Input and data validation means using an evaluation framework to judge the soundness of your data input and the effectiveness of your algorithms. Retaining professionals to create this validation assessment set is usually a necessity.

Output Validation

As with input and data validation, you will need to create an evaluation framework to judge if your output is accurate and meaningful. Flawed output means all your efforts were in vain.

Gap Analysis

Gap analysis allows you to analyze the difference between your organization’s current state and its ideal state. Your AML model should be an instrument that helps close this gap.

Record-Keeping Review

Record-keeping review helps you verify your model’s conceptual soundness while staying in compliance with AML regulators. Accurate, easy-to-locate and up-to-date records are your best weapon in the validation process.

​AML Model Best Practices

The following steps are helpful for creating your AML model using best practices:

  • Keep it Simple – Your model does not need to be complicated. Simplicity will help ensure that your employees stay focused on compliance and not on distracting bells and whistles.
  • Prioritize Quality Data Collection – You can collect so much data that it overwhelms your model and your personnel. Focus on collecting pertinent data only to better identify problem accounts.
  • Stay Up-to-Date with Customer Profiles – Knowing your customer means staying current with your customer base. You need to examine clients regularly to note possible changes to their risk profiles.

​Getting Started With AML Models

​You can design your company’s AML models so that you stay in compliance with government regulations while protecting your customers. Methods such as KYC, transaction monitoring and advanced software implementation can be combined in a model that helps eliminate illegal activity without falsely flagging legitimate transactions.

Jumio’s AML solutions can ease the burden of AML model development and validation from your organization’s management. When you are designing your AML model, you need a trusted and experienced partner like Jumio.

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