How AI Facilitates Trigger-Based AML Checks
The periodic approach to anti-money laundering (AML) checks is insufficient for risk management.
Usually, banks perform Know Your Customer (KYC) checks at onboarding, assessing the money laundering risk of a new customer. After that, they will perform periodic AML reviews of that customer, often on a one, three, or five-year cycle. This review process falls short of what is needed for risk management in the digital age. When money laundering moves at the speed of instant payments, banks need to perform checks as soon as customer risk emerges.
The answer to this problem? Trigger-driven AML checks. Trigger-driven AML checks enable banks to perform reviews whenever a customer’s risk profile changes.
But without an accurate view of customer risk, performing this kind of check is difficult, if not impossible. That’s where Artificial Intelligence (AI) technology comes into play. AI monitoring tools give banks the real-time risk intelligence they need to perform more effective and efficient AML checks.
In this article, we will explore the problems with periodic AML reviews. We’ll also explain how AI helps banks solve these problems.
Key Takeaways
- Relying only on periodic AML checks can delay the mitigation of money laundering risk
- Periodic AML checks can cause review backlogs and resource strain, resulting in analysts needing to spend too much time on unproductive activities
- AI technology gives banks a more accurate view of customer risk, helping them conduct more efficient and effective AML checks
What Are the Problems with Periodic AML Checks?
Conducting only periodic AML checks can cause the following problems:
- Delayed money laundering risk mitigation
- AML check backlogs and resource strain
1. Delayed Money Laundering Risk Mitigation
Conducting AML checks only on a periodic basis can lead to a delay in the mitigation of money laundering risk. A customer’s behavior and circumstances can change in an instant. If a bank waits months, or even years, to reassess customer risk, they miss the opportunity to put additional controls in place. This level of risk exposure can be avoided. Banks need the ability to address money laundering risk earlier.
2. AML Check Backlogs and Resource Strain
Running only periodic AML checks causes spikes in review queues, straining a bank’s resources. You can only hire so many analysts, and they have only so many hours in a day. Backlogs filled with reviews of low-risk customers can bottleneck AML processes and can even delay periodic checks. Inefficient AML check processes drain a bank’s resources and expose it to unnecessary risk. Banks need a way to streamline AML review operations so they can spend more time and attention on genuine risk, and less time on unproductive activity.
How Does Improved Transaction Monitoring Affect AML Checks?
Transaction monitoring and AML checks, while not the same, are closely interwoven. Accurate risk detection in transaction monitoring triggers AML checks. When investigators can assess customer risk with more precision, banks have the capacity to perform AML checks when changes in customer risk occur. This means they no longer need to perform AML reviews on customers who continue to have a low risk profile, thanks to up-to-date and comprehensive data.
How Does AI Improve AML Checks?
Improving transaction monitoring with AI makes both periodic and event-based reviews more effective and efficient. AI provides the following benefits to bank’s AML programs:
- Precise money laundering risk detection
- Reduced AML review backlogs
1. Precise Money Laundering Risk Detection
AI detects money laundering risk more precisely than legacy monitoring technology. It does this by analyzing more risk factors and historical decision data, using techniques like pattern recognition and anomaly detection. These AI technologies excel at identifying departures from normal behavior in transaction and customer data. When banks apply this real-time intelligence to customer risk assessments, the risk events that trigger AML checks are more accurate—and timelier. In this way, AI enables a bank to address money laundering risk sooner, improving their risk coverage and AML compliance.
2. Reduced AML Review Backlogs
AI models can scrutinize transactions with a more fine-grained lens than legacy monitoring technology, examining them within the customers’ context. Because AI can apply multiple contextual filters simultaneously, it can better determine whether a given behavior represents crystalized risk. With this understanding of customer behavior, banks can make more accurate predictions about money laundering risk. This allows analysts to perform checks only when relevant risk and data events occur. With AI, banks can avoid the massive spikes in review volumes caused by periodic checks, focusing their resources on genuine risk.
Example: AML Check Optimization
Itus Bank has a problem. Every few months, their team of analysts gets completely overwhelmed by a spike in the AML checks in their queue. The management team looks at the data and determines that their periodic review cycles are causing the spikes and slowing operations.
Once they’ve diagnosed the issue, Itus Bank begins plans to shift from periodic reviews to trigger-based reviews. However, they need the ability to identify changes in customer data and risk profiles as they emerge. They conclude that deploying AI models will help them analyze customer behavior and identify risk in an accurate and timely manner.
Itus Bank trains and deploys the AI models. The models identify genuine risk and deprioritize less risky customers. Now, Itus Bank can focus AML checks only on customers with changes in their risk profile, and they abandon periodic reviews in favor of trigger-based checks. Their AML operations run more effectively and efficiently, which gets reflected in improved results in audits and regulatory examinations.
Hawk’s AI-Powered AML Technology
Hawk’s AI technology empowers banks to run more effective and efficient AML checks. Our AI Anomaly Detection and False Positive Reduction models provide for more precise risk identification, making AML programs more effective and efficient. This technology helps banks conduct trigger-driven AML checks, reduce review backlogs, and address genuine risk sooner, improving AML compliance.
Contact us today to learn more about how we can help you improve your AML check processes.