How AI Can Help Enhance Compliance: 5 Essential AML Use Cases

In 2023, Binance, a cryptocurrency exchange, was hit with a staggering $4.3 billion fine for failing to comply with anti money laundering (AML) regulations. Many other businesses were hit with fines in the hundreds of millions, all for failing to follow the policies and regulations in place to prevent money laundering.

The fact is, money laundering has severe consequences. It causes financial losses, erodes the trust of the public, and damages economic systems worldwide. Following these regulations is important, but it is also difficult. Large companies have departments dedicated to anti money laundering, but even so, manually processing all the transactions, performing customer due diligence, and maintaining proper records is inefficient. Given the rate of human error, there is also a high chance of false positives. Plus, it’s hard to keep up with money laundering risks as they evolve with the times.

Our best bet is to leverage AI and Machine Learning to automate the most complex aspects of AML compliance and risk management. 

5 Essential AML use cases for AI technology

In this article, we will analyze five use cases of how AI can support anti money laundering compliance. Here are the most essential use cases.

Use case 1: real-time transaction monitoring

Until recently, transaction monitoring systems operated on rules-based systems. While they were reasonably sophisticated, criminals using modern technology have managed to evade them. Today’s criminals use globe-spanning systems to perform money laundering activities and hide it from authorities. Money laundering organizations use shifting patterns of transactions, and as a result, rules-based systems have become almost obsolete.

This is where AI-enabled systems come into the picture. The current screening and monitoring systems can access transactions, but the AI allows for real-time transaction monitoring. The AI/ML systems are trained on historical data, allowing them to perform significantly better. The biggest advantage of AI-enabled systems is that they reduce false positives greatly, allowing task forces to focus on high-risk transactions.

AI also makes risk assessment much easier by clearly showing why a particular transaction or set of transactions was marked at a certain level of risk. This level of transparency allows the compliance teams to justify their decisions as well as meet the compliance requirements.

These two systems, AI-enabled and rule-based, are best used in synergy. The low-priority alerts can be automatically processed using the rule-based system, while actionable data can be sent up the chain of command to be handled by the people in charge.

For example, HyperVerge’s real-time anti money laundering screening and monitoring solutions enable institutions to handle large volumes of data in real time, with a variety of features including:

• Adjustable filters to focus on high-risk countries
• Removal of irrelevant profiles
• Comprehensive risk profiles and analysis
• Real-time sanction screening and more

Use case 2: customer risk profiling, risk assessments, and pattern recognition

Another major aspect of compliance management is risk. As shown in the previous use case, AI-empowered systems are excellent at pattern recognition, even when dealing with fuzzy data, but assessing risk is another problem altogether. Older risk assessment systems have a number of issues, especially if the data fed into them is not up to the mark. Often, individual profiles with similar sounding names were considered to be the same, causing unrelated individuals to be marked as high-risk or as politically exposed persons.The AI models in use are trained with labeled data to rapidly identify targeted patterns at scale. 

With AI-enabled systems, institutions can fingerprint individual users, making sure that patterns can be tracked accurately. This process uses AI models and predictive analytics that excel at detecting specific known criminal patterns, as well as variants. This reduces the risk of false positives, and allows for precise analysis.

Another major advantage of AI-enabled software is that it is excellent at big data analysis. Given the number of transactions to analyze, institutions need to automate their processes. Earlier, manual analysis was the best bet, but many money laundering rings got away due to human error. Now, with the level of complexity that anti money laundering task forces have to deal with, AI-enabled software is a great tool. These AI-enabled systems easily identify complex patterns that may go under the radar, especially if the quantities are small and buried in completely legitimate transactions.

Given compliance requirements, accurate risk profiling is extremely important, whether for single customers or whole institutions. Anyone acting on the information needs to be able to justify their decisions properly. Proper Customer Due Diligence (CDD) requires proper risk assessment, especially when it comes to financial crime.

To that end, HyperVerge’s complete view of customer risk identification can enable any institution to protect itself from financial crime and handle compliance. This system can be used for KYC, global watchlist screening, customer due diligence, risk assessment, and more.

Use case 3: sanctions screening

When most transactions were done in person in banks, sanctions screening was relatively easier. With the level of technological advancement, challenges have increased drastically. Along similar lines, the use of digital transactions increased exponentially due to the pandemic lockdown, and a vast majority of customers have decided to continue using digital transactions due to the convenience factor.

Unfortunately, this has made it much easier for criminal organizations to use these methods to expand their operations, and to counter that, regulatory authorities have become stricter when it comes to compliance. On top of that, political issues and wars are leading regulators worldwide to regularly update their lists of sanctions.

All these factors have started putting heavy pressure on any industries that need to comply with sanctions or other such AML regulations. People with the right skill sets are few and far between, and building a team of people with the right level of expertise is expensive. On the flip side, failing to comply with sanctions can be ruinously expensive.

Since all financial transactions are subject to sanctions screening, compliance teams have made heavy use of rule-based approaches. In the past, when transactions were fewer, it was feasible. 

Today, with the torrent of data that these teams have to deal with, this is no longer the case. The costs of compliance are rising, and so are the fines.

The last, and most important factor affecting sanctions screening is speed. Manual analysis is slow and error-prone in today’s world, when customers expect worldwide transactions to happen in a matter of seconds.

This is where AI comes in.

Regulated institutions have started using smart screening, which refers to a combination of AI-powered systems and rules-based systems. AI-powered sanctions screening has a number of advantages over manual screening, and the most important of those are scalability and accuracy. AI-powered sanctions screening allows for efficient and accurate compliance.

HyperVerge’s AI sanctions screening system enables financial institutions to perform a comprehensive analysis of all financial transactions, scanning for every single kind of sanction, including:

• Comprehensive sanctions
• Targeted sanctions
• Sectoral sanctions

It can also scan for sanction lists made by sanctioning bodies like the United Nations, OFAC, EU EEAS, or the UK Sanction List.

Use case 4: suspicious activity reporting

One of the first lines of defense against money laundering is the process of suspicious activity reporting. A Suspicious Activity Report (SAR) is a document that financial institutions use to record any transactions that have any red flags suggesting money laundering, terrorist financing, or other such financial crimes. These documents are then filed with the relevant authorities, allowing them to analyze the situation further and escalate if need be.

Like the other use cases in this article, SAR is extremely important, but hard to do at scale. It is literally impossible for any single team to track and process every single kind of suspicious transaction, and even rules-based systems can be circumvented with a little bit of tweaking.

For example, all transactions over $10,000 are considered to be reportable. But if one account routinely deposits money just under the threshold, it can be considered suspicious. However, if that account were to bury the transactions in a number of legitimate transactions, it is possible to miss it if the appropriate rules are not set in place. 

Right now, SAR compliance has multiple complications, including:

• Data Volume – Analyzing every single transaction in an actionable time frame is almost impossible.
• Lack of Resources – Proper analysis requires a lot of time, experts, systems, and money.
• Identification – Proper identification of suspicious activities requires skill, and any transactions that are missed can result in sanctions.
• Global Coordination – Different countries have different AML policies, and coordinating on a global scale has many challenges.

Currently, AI-enabled systems are changing the game. There are four major advantages that AI-enabled systems can give, namely:

• Improved accuracy – AI reduces human error and both false positives and false negatives, since it can analyze large volumes of data 
• Improved efficiency – Real-time analysis can throw up red flags as soon as a pattern is spotted, which is much faster with AI.
• Improved Adaptability – AI-enabled systems can use machine learning to adapt to new methods used by criminal organizations, and can use pattern matching and fuzzy logic.
• Easier Global Coordination – AI can help generate reports tailored to the different sets of AML laws and policies across the world, making inter-governmental cooperation easier.

These AI-enabled systems also massively improve customer experience, since fewer transactions give off false positives. This can lead to increases in efficiency across the whole organization, since there is less pressure on compliance teams.

HyperVerge’s AML solution can give you these advantages and more, through features like automated detection and analysis, seamless integration with current financial systems, and advanced data management for compliance and transparency. 

Use case 5: perpetual KYC (pKYC)

Know Your Customer (KYC) systems have been instrumental in the fight against money laundering and terrorist financing. By making sure that every single customer is who they say they are, AML task forces have vastly reduced the capacity of criminal organizations to transfer money secretly.

Unfortunately, the threat posed by these organizations is always evolving, and they have developed a number of countermeasures for KYC systems, increasing money laundering risk. One major flaw in older KYC systems was periodic checks. Customer KYC documents were examined after fixed periods, and any changes in the meantime could not be analyzed.

As a result, ongoing customer due diligence became necessary, leading to the creation of Perpetual KYC (pKYC).

Perpetual KYC operates dynamically, constantly scanning for any changes in customer profiles. By connecting to identity databases and performing real-time scanning of all transactions, pKYC systems can instantly flag any suspicious changes to the customer’s profile.

For example, if a scan shows that a customer has been in contact with or has become Politically Exposed Persons, the pKYC system can flag them as someone who needs to be analyzed closely to avoid any money laundering problems.

Similarly, if an organization is analyzed and the pKYC system learns that the Ultimate Beneficial Owner has changed, it can mark it as a potential shell company, among other possibilities.

Flagging such changes in a customer’s profile is done using event triggers. When the AI detects large changes that can have financial repercussions, it flags them based on the anti money laundering laws and policies set in place. These critical alerts can make the difference between catching suspicious transactions and failing AML compliance efforts.

Perpetual KYC systems also help with proper risk assessment, as these triggers can be set up to prompt manual reviews of anyone suspected of money laundering or other illegal activities. However, these systems have to also make sure that any Personally Identifiable Information is properly protected.

Final Thoughts

Artificial Intelligence and Machine Learning systems are growing in popularity, and for good reason. The increases in efficiency, accuracy, and risk management are extremely useful, and they have become a necessity given the amount of data that the average financial institution has to sift through.

Current AML technology needs to use AI/ML to keep up with compliance requirements. Plus, given the fact that the number of digital transactions keeps increasing on a yearly basis, manual analysis will soon become completely infeasible. As a result, it is vital that more institutions use AI-enabled systems to future-proof their AML framework, or they risk falling prey to money laundering organizations or strict sanctions.

To avoid this Catch-22, click here to take a look at HyperVerge’s AML solutions, and book a demo.