16.06.2025
10 min read
Current Regulatory Landscape
Regulators have fined financial institutions close to 20 billion dollars for Anti-Money Laundering (AML) shortcomings and regulatory violations in the last 6 years.
On December 11, 2020, the Senate passed the Anti-Money Laundering (AML) Act of 2020, which aimed to modernize AML/CFT guidelines by embracing technology and innovation, streamlining low-value processes, and eliminating obsolete regulations and guidance. This legislation accomplished its objective by implementing requirements that federal regulators appoint an Innovation Officer, establish a Bank Secrecy Act Advisory Group subcommittee on innovation and technology, FinCEN maintain emerging technology experts, and undergo a mandatory financial technology assessment by the Department of the Treasury.
Financial institutions have a crucial role in thwarting money laundering and terrorist financing infiltrating the financial system. They also face rising pressure to meet their compliance requirements, with increasing volumes of transactions, outdated technology, and human error. Additionally, the COVID-19 pandemic has mounted even more pressure on compliance teams to become more vigilant, as banks seek to quickly mitigate the financial impact of the pandemic and cybersecurity for remote employees.
Establishing a robust and sound Compliance program is one of the powerful deterrents against financial crimes; and, when implemented correctly, this program can minimize risk and prevent regulatory fines. Even though almost all financial institutions have implemented a program that mitigates exposure to money laundering and terrorist financing, often times these institutions fall behind with implementing the rapidly changing guidance and requirements by regulatory bodies. Compliance programs are becoming more complex in order to successfully meet these changes, incorporating advanced monitoring solutions, artificial intelligence, and empirical support all the while balancing overhead and resources.
The increase usage of machine learning, emerging technological innovations, and sharper regulatory scrutiny are driving change in combatting money laundering and terrorist financing. Where the traditional approach requires extensive manual interaction from humans to monitor and sift through false positives or true hits, the introduction of machine learning in the AML space has revolutionized the way financial ecosystems work. Machine learning can be configured to detect various typologies, trends, and behaviors, in order to track previously hidden patterns of customer behaviors and transactional activities. When compared to the traditional approach, machine learning generates lower false positives, reduces overhead, and decreases low-value manual work.
Both technology companies and financial institutions are actively designing innovative solutions like machine learning to better assess the riskiness of transacting with high-risk geographical jurisdictions, identify potentially suspicious movement of funds, and detect anomalous patterns in the transactions. Financial institutions are also investing in advanced analytics capabilities using intelligent algorithms that allows to convert raw data into valuable insights. Additionally, many are adopting new technological solutions for deep-dive analyses and dynamic reports, and creating new data integration and visualization tools that provide more thorough testing, in order to better identify trends and aberrations of complex criminal patterns.
Individuals involved in financial crimes and terrorist financing often change their tactics quicker than the regulators can keep up. The increased usage of online banking, electric payments, and virtual currencies assist in remitting and legitimizing illegal funds, which pose a threat to financial institutions in aiding these transactions and risk regulatory fines. There is a constant need for financial institutions to continuously improve and maintain their monitoring systems to detect and address suspicious activities in real-time.
Virtual currencies are fairly new, but have integrated into the financial system at lightning speed since their inception in 2009. However, financial institutions fall behind regulating them, and criminals benefit from these shortcomings. The Office of the Comptroller of the Currency (OCC) recently provided a “green signal” for financial institutions to provide fiat bank accounts and virtual currency custodial services to virtual currency businesses. Institutions serving virtual currency exchanges can now engage in using new technologies, like independent node verification networks (INVNs) and stable coins, to perform bank-permissible functions, such as payment activities, to address risks and risky behavior associated with virtual currency transactions: advanced technology, geographical risk, transaction size and frequency, transaction patterns, and source of funds. On January 14, 2021, FinCEN extended the comment period for a proposed rule aimed at closing anti-money laundering regulatory gaps for certain convertible virtual currency and digital asset transactions. In order to keep up with ever-changing technological and regulatory advances, we implement innovative financial solutions over more traditional ones, but this also adds to financial crimes and/or terrorist financing risks.
Apart from virtual currencies, marijuana-related businesses (MRBs) is another market that also is exposed to money laundering. Most MRBs struggle to find a bank willing to provide basic depositary and other financial services to them. This is due to the curious legal status of marijuana as a federally prohibited controlled substance, but a legal and highly sought-after commodity under the laws of most U.S. states. Currently, thirty-five states, the District of Columbia, Guam, and Puerto Rico have all legalized the use of marijuana to some degree. As most MRBs cannot obtain traditional banking services, these businesses must deal with an extensive amount of cash. Thus, the associated compliance costs and risks associated with MRB banking still can be substantial. This poses a fascinating mix of opportunity and risk from an AML perspective: although MRBs are no longer illegal under federal law, entities must obtain licenses under a federally sanctioned state law regime.
With the rise of online gambling, the Casino industry is seeing an upward trend in online betting. More and more states are legalizing online gambling, including Delaware, Michigan, New Jersey, Pennsylvania, and West Virginia. Social distancing protocols instituted as part of the COVID-19 response have further resulted in building closures and increased the popularity of these online platforms. While online gaming may be a new digital industry, their compliance responsibilities are still the same as that of a traditional casino. Casinos have been defined as “financial institutions” under the BSA since 1985. Casino (online or offline) also must file Suspicious Activity Reports (SARs) when it knows, suspects, or has reason to suspect that a transaction or attempted transaction is linked to illicit activity, or when it is simply inexplicable.
There is a plethora of tools currently on the market that provide automated transaction monitoring and machine learning capabilities. Often, the transaction monitoring program is not relevant to the overall BSA/AML risks faced by the bank, is outdated, or incorrectly calibrated. Some reasons for this misalignment are that the transaction monitoring program has not been revisited or reviewed since its implementation or BSA/AML risk assessment review.
As risk assessments are typically conducted on an annual basis, they are often updated only when a “major event” occurs, such as a merger or acquisition; exponential growth in a new market area; introduction of a new product or service; and/or significant changes in the regulatory environment that could impact a financial institution. A financial institutions’ policies and procedures, such as the risk assessment, should clearly detail the downstream impact created by these “major events.”
Changes to a risk assessment should also trigger an independent risk coverage assessment to be revisited for its applicability, which could further warrant a fresh round of rule scenario tuning exercises; a coverage assessment is a deep-dive analysis of the conceptual soundness of the model, a firm’s risk exposure, and risk coverage. The assessment determines if there are any gaps in coverage that exposes the firm to suspicious activity and/or deficiencies in regulatory guidance. Some examples of what the assessment reviews includes AML typologies, current rule scenarios, and coverage for “red flags” listed by the regulators.
Regulators are looking for financial institutions to implement an effective risk-based approach within their BSA/AML Compliance program, and it is the responsibility for the financial institution to have sufficient controls when reviewing their overall risk profile on a periodic basis. Demonstrating that they have thorough knowledge of the risks associated with their products, geographies, and customers from both a qualitative and quantitative perspective becomes a priority especially given the dynamic and fast changing environment. This understanding is fundamental for financial institutions to define and manage an effective BSA/AML program that limits exposure to risks associated with money laundering and terrorist financing. Matrix-IFS provides advisory services to some of the largest financial institutions in the world such as: Deutsche Bank, UBS, Rabo Bank, Mega Bank, Interaudi Bank, and many more. Here are some of the testimonies we received from our clients:
Matrix-IFS has not only helped global top-tier financial institutions implement and upgrade their risk, fraud, and compliance solutions, but is a recognized partner of Actimize, Oracle, SAS, FISERV, BAE Systems, Bottomline, and Pitney Bowes. Additionally, Matrix-IFS developers also have hands-on experience implementing out-of-the-box rule scenarios and creating custom rule scenarios that match the customer type and risk profile for these institutions. As part of every implementation project, the Matrix-IFS engagement team also creates documentation, conducts user testing, and provides training and knowledge transfer to the client’s business and technical teams. Matrix-IFS also offers proprietary tools as additional services that assist with threshold tuning for Actimize and Mantas rule scenarios, country code tool for improving geolocation on transactional data, and red flags library for assessing risk coverage. This coverage assessment framework allows for the easy identification of applicable red flags/typologies based on the products and services offered, customer types served, and geographies operated in for each financial institution.
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