Money Laundering Threat of Corporate Vehicles and How to Minimize Your Risk

Charles (Chuck) Subrt is the Director of Aite-Novarica’s Fraud & AML practice, and he covers anti-money laundering and compliance issues. Chuck spoke at NICE Actimize’s ENGAGE virtual customer event this year, on a panel titled, “ Seeing through the Corporate Facade – Money Laundering Threats in Corporate Banking”. Click here to learn more and watch this industry-leading financial crime event on demand.

Criminals continually find new ways to expand their operations and launder illicit assets while also deploying traditional methods. Financial organizations and their anti-money laundering (AML) operations are tested to detect financial crime, evidenced by the high volume of suspicious activity reports and the low rates of identification and recovery of illegitimate funds. Many organized crime groups exploit the inherent facelessness of corporate entities, complicating financial organizations’ investigations to find the true actors behind illegal activities.

The perpetrators behind political corruption, fraud, human trafficking, cybercrime, drug trafficking, and many other crimes continue to profit handsomely; they need to be able to mask their illicit activities and shelter and move around their expanding wealth. Nefarious actors exploit a variety of corporations, partnerships, trusts, and other business entities to operate with anonymity. These structures are often simple to form, maintain, and operate. Many jurisdictions do not require substantial information or documentation, especially on ultimate beneficial ownership (UBO), officers, directors, and other key stakeholders. True individual ownership and control are easy to hide beneath complex corporate and organizational layers spanning multiple jurisdictions.

Outside of publicly traded companies, information is not always readily available or accessible for private firms, partnerships, and trusts. This is particularly true for those legal structures established in foreign jurisdictions, many of which are committed to providing for the utmost corporate secrecy. Moreover, corporate structures can foster illegal enterprises with a sense of legitimacy, especially as illicit gains can be co-mingled with lawfully earned funds.

The ever-evolving regulatory landscape further complicates the inherent challenges AML organizations face when dealing with corporate structures. Numerous regulatory regimes around the world are pressing for greater corporate transparency. Many are requiring that financial organizations identify the UBOs of corporate customer relationships. Still, UBO identification can be a highly demanding task, given multifaceted and often opaque corporate structures. It is easy to miss information or be misled as to the true owners or controllers of the corporate entities, increasing the already high AML regulatory burden and pressures to get it right. 

Financial organizations exhaust billions of dollars annually on people, processes, data, systems and tools to fight financial crime and comply with a complicated AML regulatory landscape. The task of constructing accurate understandings of customers and their risk profiles is resource-intensive and extremely complex, particularly when organized crime groups hide behind opaque and interconnected corporate structures:

• Many financial organizations lack a complete and holistic view of customers and their accounts, transactions and financial crime risk.
• Harnessing data has long been a major obstacle. The data available to feed financial crime analytics, detection, and investigation is growing, but that data tends to be fragmented, siloed and incomplete.
• Legacy, rules-based AML transaction monitoring systems can have limited effectiveness. They often fail to spot questionable activities, networks and relationships. These systems can generate a high volume of false positive alerts, inundating AML operational units and driving up operational costs.
• AML processes, particularly around onboarding corporate customers, are heavily manual and resource-intensive. Onboarding corporate entities can take between one and three months, costing financial organizations millions of dollars every year.

 Extensive detection of financial crime remains a utopian dream. Organizations are often challenged to detect even a small percentage of illicit activity; many instances of money laundering and other illicit activity go undetected. Less effective and efficient, AML operations require increased resources, and achieving regulatory compliance becomes trickier. Given this harsh reality, financial organizations must innovate. AML programs must become more data-driven, analytical, and intelligent. 

Transformation is not a simple or easy endeavor. Data obtained at customer onboarding and throughout the customer life cycle represents the most important asset in fighting financial crime; financial organizations must find and embed new technologies to unlock the potential of that data. This journey often starts with modernizing the practices, approaches, and tools supporting Know Your Customer (KYC). 

A financial organization’s KYC framework can be divided into three key tasks: data collection and verification, dynamic risk assessment, and ongoing monitoring.

• Data collection and verification: Financial organizations are obligated to collect, verify, and record customer identification information based on the customer’s specific nature and risk profile. Keeping the customer information up to date enables an organization to direct its financial crime monitoring and investigation efforts better.
• Dynamic risk assessment: Financial organizations are expected to continuously evaluate each customer’s AML risks and apply enhanced due diligence commensurate with the assessed risk profile.
• Ongoing monitoring: Financial organizations are expected to review customer accounts—basing the frequency upon the designated risk profile—and maintain current customer information. Ongoing risk-based monitoring is conducted primarily to detect potential money laundering, terrorist financing, or other illicit activity. 

Uplifting the tools and platforms supporting these activities will result in enriched, multi-dimensional risk intelligence that is more responsive to new information and changes in customer behavior while driving up resource utilization and operational efficiency. A few of the available emerging technologies include:

• Intelligent automation can streamline and optimize highly manual (and often inefficient) processes. Extensive data gathering and aggregation tasks required for periodic KYC refreshes, alert reviews and case investigations can be optimized. Discrepancies and errors in customer information can be identified quickly.
• Entity resolution can triangulate and consolidate entity data from diverse sources and enrich it with third-party sources. This can produce the additional contextual intelligence advantageous for smarter and faster customer risk profiling and organizational decision-making.
• Network link analysis can find relationships and connections—often unknown or hidden—among parties, accounts, and transactions. They can identify and track UBOs and their associations and facilitate customer risk evaluations and due diligence reviews.
• Dynamic segmentation can create segments based on customer behavior. It enables more proactive, risk-based, and customer-centric approaches to KYC, ongoing monitoring and financial crime detection. 

AML organizations can overcome the inherent hurdles of dealing with corporate structures when customer data and information translates into true risk intelligence. Integrating new technologies, techniques, and tools can empower financial organizations to harness customer data more effectively, build more holistic financial crime risk profiles and recognize and adapt more quickly to the ever-changing risk landscape. Organizations can make faster, more informed decisions on their corporate customers and achieve better risk-based outcomes while optimizing resource utilization and operational efficiency.