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?
It’s not news that many economies of the developing world face barriers to financial inclusion, making it difficult for citizens to both borrow and save; but the good news is that help has arrived in the linking of mobile payments with remittances. From sub-Saharan Africa to Latin America and the Caribbean, mobile money is bringing the previously underbanked into the fold.
The penalties for not complying with ever-evolving anti-money laundering and sanctions regulations are steep and have caught the attention of bank boards and senior management, already besieged by an assortment of other competing challenges. AlixPartners surveyed a variety of institutions to uncover the top AML and sanctions-compliance concerns that financial firms must address, and to discover some of the solutions they are implementing.
On November 15, the Commonwealth Bank of Australia (CBA) held its annual general meeting (AGM), during which a small but notable protest vote emerged against the bank’s board appointments and executive remunerations.