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?
The details matter when it comes to sensitive banking data, especially relating to payments. Regulators are clamping down on banks in response to the threats posed by ever-increasing criminal and terrorist activities and are requiring specific information about not only the sender but also the recipient of a payment; fortunately solutions exist in the form of MT structured message formats.