How to bridge the gap?
There are some current non-AI AML initiatives being implemented globally that can help smaller organizations bridge the compliance gap.
Biometric identification is a more mature (and currently cheaper) technology that has been leveraged to enable more effective Know-Your-Customer (KYC) programs. There are also a variety of improved transaction monitoring systems available to smaller organizations, as well as some initiatives aimed at centralizing customer screening efforts at the government level. Even allowing AI systems to dig deeper into transactional data, rather then sharing identified risks with correspondent partners, may optimize AI benefits across industry. This approach, however, would require an unprecedented level of cooperation between institutions.
Leveraging these stopgaps and focusing on efficiency and process improvements in AML programs may be key in waiting out the prohibitive cost stage of AI implementation. While AI appears to be the next great solution to tackling money laundering, effectively managing the emerging and transitional risks of the technology learning curve will be key for the smaller players in the global financial network over the next decade.
As regulatory compliance initiatives move toward utilizing machine learning and artificial intelligence (AI), what effect might the increased efficacy of AML programs have on developing nations and small financial players without large compliance budgets? More specifically, will the eventual technology gap in the capability to detect and prevent money launderers create issues in shifting laundering risks or customer de-risking?