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On January 1, 2021, the Anti-Money Laundering Act (“AMLA”) was enacted by Congress as part of the National Defense Authorization Act (NDAA). The AML Act has made emerging technologies, such as AI, machine learning, and quantum information sciences, a national...

Disruptive technologies are no longer the stuff of science fiction. They’re not even the “next big thing.” Successful firms are using them right now to automate manual processes in their AML programs.

Pandemic disruption in 2020 prioritized the automation of anti-money laundering (AML) investigations for compliance teams. Risk related to inconsistent investigation decision-making and reporting multiplied. The danger of penalties heightened. And now, the 2021...

While this is debated, the problem persists as legacy AML technology such as transaction monitoring systems (TMS) have little to no ability to identify and assess risk created by shell companies. And while policies, procedures, and processes, if applied correctly, can protect financial institutions from becoming conduits for some fraction of money laundering, terrorist financing, and other financial crimes, identifying shell company risk continues to be elusive.

On the final day of the FIBA conference, QuantaVerse Founder and CEO, David McLaughlin, participated on the “Customer Profiling, Use of Innovative Technologies in Onboarding and Risk Assessment” panel. David Schwartz, President and CEO of FIBA, set up the panel discussion by emphasizing the importance and impact that innovative technologies have on risk assessment and the customer onboarding process.

Based on these outcomes, the bank is moving QuantaVerse AI solutions into production. Moving forward, the bank will be better equipped to find hidden financial crime risk while automating 70 percent of its AML investigation processes, enabling investigators to focus their time and talents on the most complex cases.

Prior to the COVID-19 outbreak, the 2020 outlook was an encouraging one as the year was shaping up to be positive for many industries. At the mid-year point of 2020, the world has changed, and we continue to observe the pandemic’s impact on our communities, economy,...

Financial institutions are held accountable by regulators to ensure they are taking a risk-based approach in their AML/BSA compliance operations. As such, institutions must consider AML risk based on certain types of customers and transactions, including risky jurisdictions impacted by political or economic unrest.

The AI-powered QuantaVerse Automated Volume and Value (V&V) Transaction Analysis solution provides risk managers with better insights into variances in account activity that might indicate risks of financial crimes, or that suggest an account is being used for something other than its stated purpose.  Analysis of this nature is a growing regulatory burden driven by the expectation that FIs understand the risk profile of clients as well as their clients’ clients.

Financial institutions have for years banked on rules-based transaction monitoring systems (TMS) to root out money laundering and other financial crimes, only to be served up copious false positives that result in paralyzing inefficiencies, runaway investigation costs, and unseen false negatives that represent serious risk to the institution.

Packaged financial crime solutions are built from the ground up by proven AI experts working alongside experienced AML professionals. Unlike other AI for AML approaches, packaged AI solutions are quick to implement and are continuously updated by the solution provider based on input from multiple customers and regulators.

Serving almost 20 million customers, the bank was concerned about the risks associated with false negatives that its current AML compliance technology was missing. Intent on driving financial crime out of its operation, the bank began searching for a solution that could enhance its existing rules-based transaction monitoring system (TMS) and minimize the risk related to undiscovered financial crime.

While all agree the promise of AI for AML is great, taking the steps to select and implement AI within an institution is relatively new. So, what is the best approach for implementing AI and machine learning into your efforts to combat financial crime?

Over the course of the last two years, we’ve seen regulators and financial institutions make great strides towards thwarting more global crime through the identification of money laundering.

There are more than one trillion U.S. dollars are estimated to be involved in worldwide acts of bribery and political corruption each year.

Recent advances in technology and an increasing internal focus on efficiency and effectiveness present an opportunity for compliance professionals to yield significant organizational benefits.

Financial institutions around the globe continue to face scores of risks related to money laundering, terrorism financing, human trafficking, drug trade and other financial crimes whereby funds are illicitly filtered through the banking system.

Organizations today face a number of challenges and risks which has long been accepted as part of their natural course of business. Fraud detection/prevention is one of those challenges as organizations are continuously battling fraudsters and rogue employees who target their assets, proprietary information, and profits.

From bootleggers hiding profits of their alcohol sales during Prohibition to terrorist organizations using trade-based money laundering techniques today, criminals survive and succeed by constantly varying their techniques to evade detection.

Last year, 45 new FCPA-related investigations were publicly disclosed for the first time, making 2017 the most active year in history for new disclosures of FCPA-related investigations.

Human trafficking is devastating for victims, but typically low-risk for the criminals, whose activities are largely hidden from view. To disrupt human trafficking, law enforcement is partnering with NGOs, financial institutions and forward-thinking technology providers…

Creating, implementing, and maintaining an effective Anti-Money Laundering (AML) program has always been challenging, but in today’s increasingly digital, dynamic environment the task is more arduous.

While covered financial institutions are required by FinCEN to know the identity of the individuals who own or control their legal entity customers (also known as beneficial owners), the standard is increased significantly over the years.