Money laundering involves disguising the origin of criminally derived funds. To accomplish this goal, launderers often route illicit funds through transactions that are virtually indistinguishable from legitimate transfers.
As the number of online financial transactions, both licit and illicit, has skyrocketed in recent years, so has the work of anti-money laundering (AML) compliance professionals and along with it, the need for financial institutions (FIs) to adopt better mechanisms to manage risk and protect themselves and their clients from financial crime.
Once considered too costly and nascent for most organizations, machine learning has developed significantly since its emergence. A growing number of compliance teams are increasingly turning to machine learning to improve their AML programs, help manage risk, and enhance other critical business functions.
Traditional Anti-Money Laundering Systems
Most financial institutions use a transaction monitoring system for AML compliance. Traditional transaction monitoring solutions follow a set of pre-programmed rules and are known as rules-based systems. These systems generate alerts when the system detects activity that contravenes pre-set rules. For example, many institutions set up their monitoring systems to flag any transaction above $10,000 or another regulatory threshold. However, these rules aren’t always predictive of money laundering and result in high numbers of false positives, often overwhelming AML compliance teams who must then review every alert.
As a result, traditional transaction monitoring systems are largely insufficient at detecting true incidents of money laundering. In addition to using prescribed rules that generate a lot of false positives, these systems must be adjusted periodically to account for changing criminal methods. This is also highly ineffective as it increases the likelihood that real matches will be missed and that suspicious activity reports (SARs) will not be filed in time.
Analyzing mountains of transactional data and making accurate predictions at a large scale is a nearly impossible task for humans to do without the right technology. The rules-based transaction monitoring systems that most financial institutions have come to rely on are simply too rigid to adequately manage risk.
Machine Learning: What Is It and How Is It Used in Anti-Money Laundering?
A branch of artificial intelligence, machine learning refers to the capability of a machine to imitate intelligent human behavior. More specifically, machine learning uses mathematical models of data to help a computer learn without requiring direct instruction or explicit programming. This enables a computer system to perform distinct tasks on its own, adapt by incorporating new information, and continue improving based on experience. In this way, machine learning can mimic human intelligence. As a result, machine learning is a potent and versatile tool that can identify patterns, make predictions, and provide accurate results. Machine learning’s ability to model or emulate how an institution will react to certain conditions can enable decision-makers to create more informed strategies.
When it comes to AML compliance, machine learning can be used all along the AML chain, including client risk rating at onboarding, client screening, and transaction monitoring. Because an AML machine learning tool can learn complex transaction patterns, it allows financial institutions to proactively monitor customer behavior, identify anomalies in real-time, prioritize alerts, reduce the number of false positives, more accurately identify money laundering, and even prevent fraud.
Machine Learning in Transaction Monitoring
Machine learning in AML is most effective when there is a lot of leeway in selecting data attributes such as name, address, social security number, etc., and when there is an ample amount of quality data available. Therefore, machine learning in AML is especially useful in transaction monitoring.
Machine learning models study the behaviors of customers and build this information into their transaction monitoring system. Machine learning models used in AML can take large sets of data, learn from what is encoded in the data, and identify patterns of activity that indicate evidence of money laundering. And unlike rules-based models, engineers do not need to program new rules when criminal methods change.
Machine learning models operate by using an algorithm that analyses huge sets of transaction and behavior data, taking into account types of transactions, their monetary value, the parties involved, frequency, time of day, and other information. The more complex the model, the more variables it can incorporate. That information then gets used to build a profile for each customer.
Additionally, the machine learning model begins to identify what behaviors are normal and what behaviors are out of the ordinary for a customer. In this way, machine learning models can give financial institutions a much more complete profile of their customers’ activities, enabling individuals or transactions to be flagged with greater precision. The alerts generated get more and more accurate as the machine learning tool learns from customer data.
Other Ways in Which Machine Learning Benefits FIs
Besides helping to detect financial crime and improving an institution’s regulatory compliance, machine learning can benefit FIs in several other ways. Namely, machine learning can:
- Streamline customer support and provide a more personalized customer experience
- Enable faster decision-making
- Reduce costs
- Provide more effective risk management
- Enhance fraud detection and prevention
- Improve cybersecurity measures
- Automate tasks
- Improve document processing and management
View our blog on AML automation for additional information on how automated processes can help simplify compliance.
How to Implement Machine Learning in Your Institution
The following protocol outlines some of the basic steps and considerations institutions will need to consider when adopting machine learning.
- Review the available machine learning solutions and choose the one most suitable for your organization and needs
- Decide where within the organization machine learning will be used and how, taking into consideration the amount and availability of reliable, high-quality data
- For example, decide if machine learning will be used solely in transaction monitoring, across the entire AML function, or in conjunction with other areas such as fraud or cybersecurity
- Clearly outline your expectations and the results you want to achieve
- Engage critical stakeholders from the beginning of the project and obtain buy-in
- Develop a schedule and transition plan
- Define specific and actionable performance and monitoring requirements
- Develop new frameworks and protocols and revise existing policies and procedures
- Train staff
- Hire experts or other qualified staff as needed
Conclusion
In summary, machine learning in AML can help FIs to analyze vast amounts of data, monitor large volumes of transactions, prioritize alerts, reduce false positives, and even uncover other threats by finding new connections between data.
Manual processes, cumbersome procedures, and outdated systems simply aren’t sufficient to prevent money laundering and manage financial crime risks. The future of anti-money laundering lies in machine learning and similar technological solutions that can quickly and effectively sift through large amounts of data and produce high-quality, accurate information.
Regulators support these efforts and institutions are encouraged to test and implement new approaches to combat financial crime.
Machine learning is a powerful and impressive tool that can take your financial institution’s compliance program to the next level. Learn more about Alessa’s machine learning module and the benefits it can bring to your compliance program.
If you’re considering deploying machine learning in your organization or want to learn about the latest solutions to help your AML compliance team, contact one of our knowledgeable and experienced representatives.
