An essential and critical function within all financial institutions both domestically and globally is to comply with anti-money laundering (AML) and Bank Secrecy Act (BSA) laws. As part of that function, financial institutions (and other regulated entities) are required to have systems in place to adequately detect and prevent the movement of illicit funds through their institution. While AML and BSA have traditionally been associated with the consumer-facing side of banking, the landscape for institutional clients is significantly more complicated. For businesses and institutional clients, AML, BSA, and sanctions compliance involve navigating complex regulatory frameworks, handling high volumes of complex transactions, and managing the nuances of cross-border payments, sophisticated money-laundering techniques that are constantly evolving, and large-scale financial operations.

In this article, we explore the unique challenges faced by institutions in their AML efforts, the technological solutions that address these complex issues, and the ways in which artificial intelligence (AI) and machine learning (ML) can assist in tackling these challenges.

The complexity of AML for institutional clients

While most retail AML solutions focus on individual accounts, institutional AML presents a far more challenging set of problems and considerations. Businesses and institutional clients, such as corporations, private equity firms, and foreign and domestic banks, often deal with much larger, more intricate financial transactions that span across multiple jurisdictions and financial instruments and products. Some key challenges faced by institutional clients include:

1. High volume and complex transactions

Unlike retail banking, where transactions tend to be relatively straightforward, institutional transactions are often large and complex. Corporate accounts frequently involve multi-party transactions, frequent cross-border payments, and the movement of significant sums of money across various financial instruments such as syndicated loans, structured products, correspondent banking, or foreign exchanges.

These types of transactions are much harder to identify, track, and monitor, with an increased risk of money laundering and other serious financial crimes due to the sheer volume and complexity. For example, a corporate loan structure involving multiple financial institutions and jurisdictions requires in-depth scrutiny to ensure that illicit funds are not being laundered through legitimate business activities.

2. Complex legal and regulatory compliance

The current regulatory environment surrounding institutional clients is far more intricate than for retail customers. Institutions are required to comply with a wide range of local, state, regional, federal, and global AML regulations, such as the U.S. BSA, European Union directives, the Financial Action Task Force (FATF) recommendations, and other international standards. Navigating these regulatory frameworks requires not only in-depth knowledge of the specific requirements but also an ability to adapt systems and processes to ever-changing rules.

For institutional clients, the compliance landscape often extends beyond the institution itself to include compliance at the level of their clients and customers, vendors, and partners. This adds a significant layer of complexity, as institutions may need to monitor transactions that are indirect or involve third parties located in various jurisdictions with complex levels of AML enforcement.

3. Cross-border transactions and jurisdictional issues

AML compliance becomes even more challenging when institutional clients are engaged in cross-border transactions. These transactions may involve entities in multiple countries with different laws and financial regulations, which can lead to a lack of transparency and coordination between jurisdictions. The varying standards for monitoring and reporting suspicious transactions across borders create gaps that money launderers, terrorists, and other financial criminals can exploit.

Furthermore, many financial transactions today take place in markets where the regulatory environment is not as well-defined or strictly enforced, making it even harder to identify illicit activities.

The role of technology in institutional AML solutions

Given the complexities associated with institutional AML, relying on traditional manual processes or basic transaction monitoring tools is no longer enough. Financial institutions are increasingly turning to advanced technology solutions to improve the efficiency, accuracy, and effectiveness of their AML efforts. Technologies like AI and ML are revolutionizing the way institutional clients approach compliance. Now is the time for all institutions to embrace this technology and take this necessary step to move forward in the right direction to truly enhance their compliance responsibilities.

1. AI-driven case management and transaction monitoring

AI can automate the transaction monitoring process, analyzing vast amounts of complicated transaction data in real-time to identify suspicious patterns that would be difficult if not impossible for humans to detect. By leveraging machine learning algorithms, monitoring systems can be trained to recognize financial crime and money-laundering techniques specifically tailored to institutional clients, such as layering and integration, or the use of complex financial products.

For example, an AI-based tool that helps AML analysts investigate suspicious transactions by analyzing historical data and flagging potentially suspicious activity. This tool can be particularly valuable for institutional clients who deal with complex financial products and cross-border transactions, automating many of the manual tasks traditionally involved in investigating suspicious activity and allowing analysts to focus on high-priority cases.

2. Data parsing and integration with existing systems

In institutional AML, unstructured data, such as SWIFT messages, ACH payments, and financial reports, presents a significant challenge. To detect complex financial crimes, terrorist financing and money laundering effectively, institutions must be able to parse and structure this data to integrate it into their transaction monitoring systems.

AI-powered tools as described above can parse these complex data streams and integrate them into AML systems for analysis, eliminating much of the time-consuming manual work. This integration assists institutions in maintaining comprehensive records and improves compliance by ensuring that all data is captured, analyzed, and fully documented.

3. Predictive analytics and risk scoring

One of the most powerful features of AI and ML in AML is the ability to predict and assess risk. By analyzing historical transaction data, these systems can build risk profiles for institutional clients, factoring in the size, complexity, and geography of transactions. These risk profiles help financial institutions in performing a holistic risk assessment and prioritize which transactions to investigate further.

For instance, a corporate client in a high-risk jurisdiction or with frequent cross-border transactions might be flagged as high-risk, prompting a deeper investigation into the nature of their transactions and any possible links to money laundering.

4. Automation of suspicious activity reporting (SARs)

The generation and reporting of suspicious activity reports (SARs) is a crucial component - perhaps one of the most important components - of institutional AML compliance and an effective and adequate AML program. However, manually preparing these reports is often time-consuming and prone to human error. GenAI can assist in the generation of SARs, ensuring that reports are accurate, complete, and submitted in a timely manner. AI-driven systems can also help identify the right threshold for triggering a SAR, ensuring compliance with federal and state law, as well as regulatory standards, without overburdening compliance teams with unnecessary reports. AI is already assisting institutions with filing more effective SARs and reducing the filing of defensive SARs.

Conclusion

AML for institutional clients is far more complicated and nuanced than for retail clients due to the scale, complexity, and global nature of transactions involved. Traditional manual monitoring systems are no longer sufficient to detect complex financial crimes and prevent money laundering at the institutional level. By leveraging AI, machine learning, and automation, financial institutions can enhance their AML efforts, improving accuracy, efficiency, and compliance.

AI-powered transaction monitoring tools are essential in today’s regulatory environment in addressing the unique challenges of institutional AML. These technologies not only assist institutions meet their regulatory obligations but also provide a more robust and effective framework for protecting the global financial system from illicit activities.

As the financial industry continues to evolve, the importance of advanced AML solutions for institutional clients will only grow. By embracing these innovative technologies now, institutions can ensure they remain ahead of the curve in combating money laundering and other financial crimes.

Written by:

Zia Siddiqi
VP, EXL

Co-author:

Richard Weber
Former Chief of IRS, Criminal Division