US, UK Announce Machine Learning “Contest”, But Is It Too Much Too Soon?

US, UK Announce Machine Learning “Contest”, But Is It Too Much Too Soon?

As a growing number of countries have come together since the start of the new year in rebellion against Russia’s unprovoked invasion of Ukraine, the United States has continued to expand its partnership with the United Kingdom (UK) in particular, in this instance to continue pushing the fight against global money laundering efforts. With the adoption and mass utilization of artificial intelligence (AI) seen across various aspects of modern day society, the two nations have come together in an unprecedented attempt to spur innovation with respect to development of machine learning technologies that could be used to combat cross-border financial crime.

               Despite the growth of anti-money laundering (AML) and counter-terrorism financing (CFT) regulatory standards and legislation around the world over the past two decades, it is estimated that nearly $2 trillion worth of international money laundering still takes place on an annual basis. The destabilizing effects of these efforts can be felt in nearly every corner of both the developed and undeveloped world and can contribute to the proliferation of other illicit activities such as the drug trade, human trafficking, and organized crime. With these pervasive threats growing in stature, early last week, government representatives of both the US and UK formally announced a joint initiative to collaborate on a groundbreaking “prize challenge” program that will aim at developing privacy-enhancing technology (PET) and the development of trained software to better detect trends and lead to improved apprehension of those behind countless forms of illicit financial activity. Given the aforementioned increase in scope of domestic and international AML/CFT requirements for covered financial institutions (FI’s), many businesses have had to commit significant amount of funds and manpower to their compliance department’s avoid regulatory slip-ups (not to mention avoiding becoming cogs in the crimes these regulations seek to limit). Until relatively recently, bloated compliance departments manually sifting through data have wasted valuable resources, time, and funds which in turn have taken away from development in other areas of a FI’s daily operations. While increased investment into AI and machine learning capabilities are still in their infancy, the possibilities for banks once adopted are endless. These processes could rapidly and accurately sort through a treasure trove of pertinent financial and personal information, streamlining onboarding, customer due diligence and risk management requirements for banks while ensuring the integrity of the firm’s operations and that of the national financial system in a respective jurisdiction by identifying and subsequently analyzing potentially suspicious activity.

               A significant obstacle to widespread adoption of machine learning however has been a gross lack of access and sharing of customer’s personal information. The Wall Street Journal writes that while “governments have encouraged financial institutions to create information-sharing partnerships to improve their reporting activity and make the data more meaningful, data-privacy rules create challenges for doing so.”1 As such, the ability to “train” monitoring systems utilizing data from various entities/sources without violating said privacy safeguards is the goal of the new program and has been coined “federated learning.” However for such an initiative to be successful, there would also likely need to be a gradual relaxation of these restrictions within a controlled environment that would be secure and as such prevent any accessed information from leaking out – which is quite a challenge in itself.

               All told however, this novel strategy for encouraging innovation is an outside-the-box approach that may ultimately prove very fruitful for these world powers. While full details on the program’s inner workings and the prizes involved have yet to be revealed, the contest will seemingly be open to the public and entrants will begin being taken this summer. The winner(s) will be those offering the best machine learning solutions to combat money laundering and will be announced in 2023. The move comes in stark contrast to traditional government contracted work where the government will create a new program independently and seek out select talent to bring their vision to light. In this case, they are using the private sector and the enticement of personal gain while allowing what appears to be a less exclusive practice to unfold. To steer these efforts in the right direction however, the Treasury’s Financial Crimes Enforcement Network (FinCEN) and the U.K.’s Financial Conduct Authority (FCA), as well as its Information Commissioner’s Office, will make themselves available to innovators as part of the program.

               Some however remain skeptical of the potential misuse of AI when adopted at such a large scale. Just recently, leaked transcripts of a conversation between a senior software engineer and an artificial intelligence application at Google have raised questions about the true end-game for many of the algorithm-backed AI programs being developed by software conglomerates based in the U.S. and abroad. While Google has denied claims that they are producing sentient AI, the software engineer that aired his belief that the system has the ability to perceive and express thoughts and feelings comparable to that of a human was ultimately put on administrative leave from the company for an indefinite period. With AI approaching what appears to be a greater sense of self-awareness, many have wondered just how dangerous it might become to give these programs more and more access to highly sensitive information.

               One thing remains certain however and that is the fact that global governments are adapting to major technological developments that are now seen almost daily and seeking to capitalize on their utilization for improving safety and financial stability at the international level. It remains to be seen however whether the results of such breakthroughs may be undercut by new data-privacy risks moving forward.

Citations

  1. Vanderford, Richard. “U.S., U.K. Collaborate to Spur Innovation in Tech Used to Combat Money-Laundering.” The Wall Street Journal, Dow Jones & Company, 13 June 2022. 
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