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A key part of the role of Chief Compliance Officer is satisfying both regulatory guidelines and the regulators that monitor their programs. That has meant staying the course with an AML program that checked regulatory boxes even if outcomes were less than effective....

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 false positives that result in paralyzing inefficiencies and runaway costs. Although an...

False positives caused by Volume and Value (V&V) calculations violating a rule or a threshold are a significant cause for concern among anti-money laundering (AML) teams. According to industry estimates, close to 50% of the alerts generated by transaction...

David McLaughlin’s journey in creating the QuantaVerse platform and the problems he set out to solve makes for a fascinating story. It is one in which powerful technology supports human intuition for superior outcomes that can only be achieved by marrying the...

AML RightSource has added new AI models to its QuantaVerse Financial Crime Platform. These models predict volume and value across a wider array of transaction activity profiles, fine-tuning V&V analysis and further reducing false positives.

When it comes to anti-money laundering (AML), compliance is a cost center for financial institutions and budget pressures are the norm. This begs the question: “How can institutions hold the line on costs while demonstrating their support for AML compliance?” This is...

Financial institutions around the world need to heed the warnings highlighted in The Basel AML Index 2021. The annual global study of 110 jurisdictions, which assesses money laundering risks around the world and ranks jurisdictions on how well they’re addressing them, found that the fight against dirty money remains a challenge. It also notes there is still too big a gap between promises to change behavior and actual results.

In the two decades since the 9/11 tragedy, new technologies have emerged, new laws have been introduced, cryptocurrencies are continuously changing the way money moves around the world, and a global pandemic has radically changed transactional behaviors and processes. This post brings readers up to date on how terrorist financing is evolving in line with current trends and explains what financial institutions can do to most effectively counter criminal activities. 

It’s a well-known fact that the global pandemic caused a radical shift in consumer banking and payments behavior. What isn’t as obvious is how financial institutions responded behind the scenes. Fortunately, a new study helps shed light on the pandemic’s impact on the adoption of new technologies for anti-money laundering (AML) efforts.

Regulators and those handling compliance at covered institutions have long accepted the pitiful state of AML program efficacy, including: An estimated $2 trillion laundered through the global banking system annually 90+% of false positives coming from transaction...

While not mandating that firms invest in technology to automate financial crime investigations, regulators are certainly encouraging it. They are noticing that advanced BSA/AML teams are using robotic process automation (RPA) bots to gather data for investigations. They are aware that those same firms are using machine learning to analyze huge data sets, identify patterns, and pinpoint where exceptions or anomalies exist.

Current estimates suggest that Latin American cartels who facilitate illegal U.S. border crossings net $400 million each month. These, and related windfalls, must be laundered through the financial system to facilitate other cartel business and fund the lavish lifestyles of the cartel kingpins and their senior management.

Federal bank regulatory agencies require that financial institutions regularly review and segment all their customers based on risk. While the Customer Due Diligence (CDD) Rule, which amended BSA regulations in 2018, does not stipulate how often reviews should be...

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.

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?