George Bernard Shaw once said: “Those who cannot change their minds cannot change anything.” The past week has seen a significant change of mind from financial regulators in the US in their ardent attempt to combat and prevent financial crimes, especially money laundering. On December 3, The Board of Governors of the Federal Reserve System, the Federal Deposit Insurance Corporation (FDIC), the Financial Crimes Enforcement Network (FinCEN), the National Credit Union Administration, and the Office of the Comptroller of the Currency issued a joint statement encouraging banks to use modern-era technologies to bolster their Bank Secrecy Act/anti-money laundering (BSA/AML) compliance programs. The agencies ask banks “to consider, evaluate, and, where appropriate, responsibly implement innovative approaches to meet their Bank Secrecy Act/anti-money laundering (BSA/AML) compliance obligations, in order to further strengthen the financial system against illicit financial activity.” They are of the view that private sector innovation, involving new technologies such as artificial intelligence and machine learning, can help banks identify and report money laundering, terrorist financing and other illicit activities.

In addition, the regulators assured that they will not penalize those firms who are found to have a deficiency in their existing compliance programs as they run pilots employing modern technologies. The statement reads: “While the Agencies may provide feedback, pilot programs in and of themselves should not subject banks to supervisory criticism even if the pilot programs ultimately prove unsuccessful. Likewise, pilot programs that expose gaps in a BSA/AML compliance program will not necessarily result in supervisory action with respect to that program.” They have added that “the implementation of innovative approaches in banks’ BSA/AML compliance programs will not result in additional regulatory expectations.” The regulators have been confident about the potential of new-era technologies to enhance key AML processes such as risk identification, transaction monitoring, and suspicious activity reporting. Speaking about this fundamental change in regulators’ mindset, Erin DeWitt, former Examiner at Federal Reserve Bank of Atlanta and former Chief Risk Officer at both Scottrade Financial and MidSouth Bank, N.A. said: “By issuing a joint interagency statement that banks could experiment with artificial intelligence software without fear of future regulatory criticism or future penalties if such pilots were determined to be unsuccessful, the regulatory community is publicly asserting its recognition of the need for technical innovation to effectively and efficiently combat financial crimes. The previous ‘elephant in the room,’ the fear of uncertain regulatory backlash regarding the adoption of such technologies, has been publicly removed and will be a game changer for both the banking and fintech communities. ”

The statement largely clears the air for modern AML solutions, especially those based on artificial intelligence and machine learning, as banks were reluctant to make use of them due to regulatory ambiguity surrounding them. For US banks, the directive provides clarity in their approach to pursue modern solutions to ensure compliance. They were in serious confusion over the use of these solutions, their acceptance by regulators and possible augmented scrutiny and supervision. The regulators have only one demand: “banks must continue to meet their BSA/AML compliance obligations, as well as ensure the ongoing safety and soundness of the bank, when developing pilot programs and other innovative approaches.”

The statement also reiterates the relevance of modern technology companies such as Tookitaki in this era of sophisticated financial crimes that are impossible to detect with legacy systems. Regulators were largely sceptical about the use of machine learning by firms due to the widely prevalent black box approach in the technology. Now is the time of ‘Explainable AI or Transparent AI, which provides complete interpretability of the workings of complex algorithms. We understand that the regulators are also confident that these technologies are safe to use and they can provide superior results. “These innovations and technologies can strengthen BSA/AML compliance approaches, as well as enhance transaction monitoring systems. The Agencies welcome these types of innovative approaches to further efforts to protect the financial system against illicit financial activity. In addition, these types of innovative approaches can maximize utilization of banks’ BSA/AML compliance resources,” say the agencies.

In fact, the US is not alone with its encouraging approach towards banking solutions that use modern technologies. The Monetary Authority of Singapore (MAS) always supported the use of RegTech by financial institutions to overcome their regulatory pains. The regulator has recently come up with good data analytics use cases to fight financial crime. It cites the example of a transaction monitoring solution that provides effectiveness improvements, such as the 40% reduction in false positives and 5% increase in true positives. Tookitaki had similar results with its successful pilot with United Overseas Bank (UOB), employing the company’s AML platform.

The past week has been filled with a lot of positivity and promises for Tookitaki. It is both happy and excited at the US regulators’ change of tone with regard to the use of modern technologies by banks and financial institutions to combat financial crimes such as money laundering. As the US financial world is embracing modern techniques to be more compliant with regulations, Tookitaki is looking at its possibilities to be a key player in the inevitable change. The company’s award-winning machine learning solutions, especially the Anti-Money Laundering Suite, has the potential to bring a paradigm shift in the way how current AML compliance programs are working. The solution, which has separate modules for screening and transaction monitoring, is built based on the design philosophy of increased efficiency and enhanced risk coverage while being fully transparent with the platform. The whitepaper named “The case for artificial intelligence in combating money laundering and terrorist financing: A deep dive into the application of machine learning technology” (jointly released by Deloitte and UOB) provides deeper insights into the solution and its advantages.

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