For several years now, anti-financial crime (AFC) regulations have prohibited the operation of shell banks. A shell bank is a bank that has no physical presence in the jurisdiction where it is incorporated or licensed and no affiliation with a regulated financial group.
Money-laundering activities should have received a fatal blow from the scandals revealed in such documents as the Panama Papers, but recent events paint a different picture: the offshore finance industry and money laundering continue to be alive and well! Financial institutions that find AML compliance an escalating struggle are not alone, but the costs of non-compliance are even more taxing. It’s past time for banks to take a closer look at their client portfolios.
An increasing number of European banks and their supervisory authorities are being drawn into money laundering allegations.According to the Organised Crime and Corruption Reporting Project (OCCRP) the latest allegations on ‘Troika Laundromat’
Digitally native customers are driving banks to jump into the future by embracing technological breakthroughs such as artificial intelligence, machine learning and robotic process automation. And in the process, banks are discovering the many advantages of these innovations, from cutting down on costly human errors to improving everything from fraud management, operational efficiency and trading. As they progress through their digital evolutions, many are reinventing themselves for the better.
In spite of the recent rise of protectionism amongst major trade partners, international trade growth is strong, with emerging markets providing the main impetus. Trade growth could be even stronger if not for the shortfall in trade financing supply relative to demand, a gap that is partly due to regulation compliance. Technology is coming to the rescue, not only in addressing the trade finance gap but ameliorating operations throughout trade channels.
The rapid adoption of artificial intelligence and machine learning in all corners of the financial sector, particularly in anti-money-laundering (AML) efforts, has excited and inspired onlookers and participants alike. But as with all innovations, there are pitfalls to unquestioning acceptance that can actually worsen the situations these technologies are meant to address. Human intelligence must work cooperatively and in the lead role alongside AI and ML to guarantee the best results.
Money laundering and terrorism financing are serious issues that banks must address, especially as too many financial institutions are complicit in enabling the flow of unlawful funds. Unfortunately, the need to act decisively has also resulted in a disabling tightening of trade finance, sorely needed for economic growth. The new Asian Development Bank Scorecard seeks to ameliorate the inadvertent consequences of AML and CFT compliance.
Combating money laundering is no longer a choice but a must for banks. But the effort that must go into fighting it is daunting. How can technology, especially artificial intelligence and machine learning, battle the costs and drains on monetary and human resources required for AML compliance, making the whole process a lot easier and more effective? Can AI be trusted to do the job right?
Banks are spending $20 billion on compliance in an effort to combat money laundering, yet only one per cent of illicit financial flows are seized by authorities every year. While regulations have been introduced to crack-down on money laundering, so far they have had a limited effect.
As customers age, their vulnerability to abuse, especially financial, increases concurrently. Elder financial abuse is not a new crime but is becoming more prevalent with the current senior boom. Where does the bank’s responsibility to ensure safe banking for elderly customers begin and end, and what steps can it take to ensure the financial well-being of all clients, especially its most vulnerable?