AI and machine-learning anti money-laundering solutions are now sought-after by financial institutions under pressure to meet compliance regulations.

As the world continues to fight against the COVID-19 pandemic, there are reports that criminals are taking advantage of the difficult situation to proliferate their criminal activities, earn undue profits and transfer of illegally-earned money across borders, according to one report.

According to the European Union Agency for Law Enforcement Cooperation: “Organized crime groups are notoriously flexible and adaptable and their capacity to exploit this crisis means we need to be constantly vigilant and prepared.”

Many regulators across the globe have realized the challenges that banks and other financial institutions will face in anti-money laundering compliance during and after the pandemic. AI and machine learning solutions are being sought by financial institutions to tackle the problem.

To meet the demand for stepped-up anti-money laundering (AML) vigilance, two tech companies had recently joined forces to supply next-generation solutions.  Broadridge Financial Solutions, Inc. teamed up with Tookitaki to address industry-wide reconciliation, matching and exception-processing inefficiencies.

Customers will be able to license modules on their platform for multiple Intelligent Automation applications. Said Alastair McGill, general manager of Data Control Solutions, Broadridge: “Intelligent Automation will drive performance and productivity gains from incumbent reconciliation systems, especially for organizations that have multiple vendor solutions in place. By leveraging AI and ML we are helping eradicate breaks in the exception management world, automatically finding the underlying cause of a problem and resolving it efficiently to ensure the underlying cause is addressed.”

Said Tookitaki Founder and CEO, Abhishek Chatterjee: “Tookitaki is helping Broadridge offer automatic matching and break detection with supporting audit trails. Our patent-pending explainability framework offers a ‘glass-box’ approach to ML models that allows users to view decisions made by the platform’s engine through a simple interface. This offering is unique and provides an unprecedented level of transparency to build confidence and trust in the application.”

To align with the sudden spike of online payments due to more people staying at home, banks and financial institutions will need to recalibrate their data trend and boost AML vigilance for cybercriminals leveraging on the global phenomenon.