An ongoing challenge is that the threats are ever evolving. The nature of illicit fund flows are varied and often cut across jurisdictions and economic sectors. In the case of money laundering (ML), the intention is to camouflage the origin of ill-gotten proceeds or property. Terrorist financing (TF) can be different in that the sources of such funding may be of proper or legal origins. The motivation is often to disguise the use of the funds. 6. Criminals can and will try to use increasingly sophisticated ways to “beat the system” to launder money or finance terrorism. It is very difficult to estimate the size of this problem given its opaque nature. There have been various estimates that the amount of money laundered each year is far in excess of $ trillion. In truth, no one really knows. What is more important is that globally, it is recognised that this is an important issue that affects us all. ML and TF threats undermine the confidence and stability of financial systems. There are also high social and political costs. 7. This is why at national levels, many countries including Singapore have taken holistic measures to address ML/TF risks. Besides a multitude of actions relating to laws, regulations, penalties and public awareness, specific tools also include the ability of regulators and law enforcement authorities to carry out risk surveillance, investigation and information exchanges. A number of international organisations and groupings, including the Financial Action Task Force (FATF), IMF, G20, OECD’s Global Forum on Tax, the Egmont Group etc.
This SupTech-Regtech linkage is targeted at driving adoption of technology for compliance and supervision efficiency and effectiveness. 33. A greater use of analytics to support our AML/CFT risk monitoring and supervision work will hence be a key priority in MAS. To illustrate, we expect to tap Hadoop-based Big Data system to build complex network models that are better able to identify and pick up subtle patterns in entity behaviours. In the future, we hope to rely more recent technologies such as Machine Learning, Deep Learning and Graphics Processing Unit (GPU) based computing, to deepen MAS’s financial surveillance and supervisory insights. I know that some of the major FIs are doing the same. Our Data Analytics Group will also look to establish a community of senior data 4/5  BIS central bankers' speeches  practitioners with the industry to support an exchange of views for mutual learning. 34. Let me conclude by reiterating why we take ML/TF threats so seriously. Singapore is an important global financial hub. Our financial centre is home to many FIs including a number of international firms. Many of them serve the region including in global business lines or in regional corporate functions. Our reputation is one of being a clean, trusted and efficient financial centre supported by a talented workforce and strong rule of law. This reputation has been built over the years and must be protected diligently. 35. With the rise in ML/TF risks globally, Singapore must be proactive in playing our part to combat these threats.