While nonprofit organizations advocate for policy changes to address the global phenomenon of derisking, financial institutions are creating cutting-edge technologies to speed and improve their compliance with anti-money laundering regulations.
A new study from the Center for Global Development assesses six new technologies and their potential to solve the derisking problem. Fixing AML: Can Technology Help Address the De-Risking Dilemma? examines machine learning, biometrics, big data, know your customer (KYC) utilities, distributed ledger technology (DLT)/blockchain, and legal entity identifiers (LEI). Machine learning is a type of artificial intelligence that could cut down on false alerts and identify undetected illicit finance techniques. Biometrics are much more robust than passwords or tokens and generally easier to use. Big data refers to datasets that are high in volume, velocity and variety and their applications offer more scalable storage capacity and processing. They also allow different types of data to be stored in one place, so compliance staff spend less time gathering information from disparate sources. They can greatly expand the range and scope of information available for KYC and suspicious transaction investigations. (Read more)
KYC utilities are central repositories for customer due diligence (CDD) information. They can reduce the amount of information that has to be exchanged between correspondent banks and their respondents, reducing the time banks spend conducting CDD investigations. CLT/blockchain is a way of securely organizing data on a peer-to-peer network of computers. The basic technology has potential uses beyond cryptocurrencies, including use in regulatory compliance, and can be used for securely storing and sharing KYC information, as well as for cheaper and more secure international payments. LEI uses unique alphanumeric identifiers, like barcodes, that connect to reference datasets. In many countries, these are now required by law, and they allow platforms, organizational units and institutions to refer to entities clearly and without ambiguity. As a result, they can facilitate greater automation and information sharing.
Although one or more of these regulatory technologies (RegTech) could work against derisking, policymakers must invest time in in understanding how they work, as well as examining their potential benefits and limitations. Only then can a framework that maximizes these advantages be developed.