AML and AI- Using Machine Learning to Build a Robust AML System

Anti-money laundering (AML) is no new term. The need to counter the injection of illegal money into the financial system has been a long-standing one. Businesses are familiar with how AML measures essentially depend on pattern recognition to monitor, flag, and report transactions and behavioral traits of customers.

Over time, money laundering methods have changed. Bad actors have integrated tech to try and circumvent the AML processes put in place by businesses. They often use AI to generate fake selfies and fake identities to commit fraud. Thankfully, tech is unbiased, and the same tech used by these criminals also has uses in thwarting them. According to a report by Market Research Future, the AI-based AML solutions market was valued at $2.58 billion in 2023. The market size is expected to reach $8.7 billion by 2032, growing at a CAGR of 14.48% for the  Today, we take a look at how businesses can use AI to improve their AML processes.

How to Integrate AI with AML?

One thing AI and a business’s AML processes have in common is pattern recognition. How? Well, AI models are developed using machine learning, which uses pattern recognition to train these models. A business’s AML processes are also based on establishing patterns of their customers’ behavior and constantly monitoring their habits to flag any discrepancies.

Data sets can be created using patterns based on customers’ transaction history, frequency, value, location, and more. These data sets can then be used to train AI models to monitor customer transactions and behavior. Any changes in these patterns would trigger a failsafe that would flag a suspicious transaction or customer. Integrating AI into businesses has been the trend in the last few years, with founders, managers, and other decision-makers have been trying to leverage the benefits of AI.

Benefits of Using AI in AML

Speaking of benefits, let’s take a look at the benefits of integrating AI with your business’s AML operations:

• Automation and scaling: Using AI to improve your business’s AML measures can help you partially automate the process. AI can be used to screen through data and reduce the tediousness involved with going through multiple data sets manually. With AI the screening process can be sped up exponentially.

• Reducing errors: The tedious process of going through multiple pieces of customer information can fatigue a human. This, in turn, can lead to errors in the screening process, where suspicious entities and transactions slip through the system. Legitimate transactions may get flagged too leading to unnecessary friction for the customer.

• Cost management: With the help of AI, your business can employ human resources more efficiently. Since AI can take over redundant or duplicate tasks, your business’s costs associated with human resources could be reduced, and the existing human resources could be employed more effectively.

• Regulatory compliance: The new AML/CFT regulations require businesses to take a risk-based approach when verifying customer identities. As far as AI integration is concerned, former Federal Deposit Insurance Corporation (FDIC) chairperson Jelena McWilliams has even encouraged the use of “New technology, such as artificial intelligence and machine learning,” to “better manage money-laundering and terrorist-financing risks, while reducing the cost of compliance”.

Limitations Associated with AI and AML

As with any technological development, AI too has its shortcomings. As for its usage, as we mentioned above, AI is not playing for any team. Currently, AI has both good and bad uses on both sides of the AML equation.

There is a problem with the usage of AI that businesses must be wary of. It’s data security. Since AI works off of data sets that it builds based on collected data, AI in AML can potentially end up creating large data sets consisting of confidential customer information. Should the databases containing this information be breached, a lot of sensitive data could be compromised. Such a compromise could hurt a business’s reputation, leading to a loss of clientele, as well as legal repercussions.

So, while businesses should get on the AI train to take as much benefit of the tool as possible, they must also exercise caution. AI alone cannot build a robust AI ecosystem and businesses need to take a holistic approach to build a secure environment for themselves and their customers. Taking on this task from scratch can lead to your business incurring significant costs. This is why it is advisable to employ the help of third-party service providers to ensure that your AML and KYC processes are robust and streamlined.

What are the AI Techniques used in AML?

Now you might wonder, “How exactly do I integrate AI and AML?” Well, wonder no longer. Here is a list of ways AI can be used to improve your business’s AML processes:

• Behavioral fraud detection: Since AI works on pattern recognition, it can make a record of the various types of transactions that a customer makes. Based on these records, it can flag any suspicious transactions that are unlikely to be made by a customer. Integrating AI can help capture the minor behavioral changes that might slip past a human screener.

• Document forgery detection: We’ve already discussed how bad actors often use AI to generate fake selfies, and false identity documents, and commit identity fraud using these. The likeness of these falsified images is pretty close to the real documents and some of them might even fool human eyes. With the help of AI, these checks can be improved, ensuring that even the most minor discrepancies are caught. Integrating AI into the identity verification process can also help your business handle a larger bulk of verifications in less time.

• Identity theft detection: Combining the two aspects of behavioral pattern recognition and document forgery detection, AI can also help catch cases of identity theft. Using AI can help speed up the process as well as reduce the number of false positives. This in turn can lead to a smoother user experience, which can reduce the instances of drop-offs.

Integrating AI and AML with Signzy

A business’ AML measures are a combination of multiple processes like robust KYC measures and a strong Identity Verification pipeline. While these processes need to be as thorough as possible to avoid letting any bad actors through, a balance must be struck between thoroughness and ease of use. Make your business’s verification process too tedious, and you lose customers right at the onboarding stage. Any hope of selling to them with your features goes out the window.

Thankfully, Signzy’s API Marketplace has the solution for all your KYC and identity verification needs. With our PEP Screening API and Criminal Screening API, your business can identify high-risk entities and pre-emptively avoid violating any AML guidelines. Other identity verification APIs like DL Verification, SSN Verification, and Liveness Check APIs can ensure that your business’ customers are who they say they are. Signzy’s Optical Character Recognition (OCR) API also utilizes AI to expedite fraud detection and identity verification. What’s better, is that we have even more APIs like this to help your business. Book a call with us and let us explore how we can work together.

Conclusion

AI integration in AML is still in its nascent stage where there’s a lot of room for improvement. Businesses should not knock this opportunity but they should also tread cautiously to not create dependencies on it. With bad actors misusing this technological development, it is only fair that businesses turn the tide of the fight by taking equal advantage of AI.