The MIFC initiatives that were announced yesterday would propel Malaysia into becoming a vibrant, innovative and competitive international Islamic financial services industry supported by high calibre human talent, world-class infrastructure and best international standards. Building on what we have, we need to create a global brand to be promoted more aggressively in the global arena. This will be key to penetrate markets as well as to attract global players to our shores, henceforth enhancing the viability and sustainability of Malaysia's Islamic financial system. Malaysia will embark on more strategic promotional and marketing efforts through greater collaboration and smart partnership. MIFC WILL BE OUR GLOBAL BRAND. 2  BIS Review 75/2006  On this note, on behalf of the hosts and organisers, I would like to thank the distinguished panellists and moderators, media and the respected audience for making this Forum a success, and I hereby declare the forum officially closed. Thank you. BIS Review 75/2006  3
No hard data on industry-wide uptake are available but intelligence from supervisors is that the scale of adoption of advanced analytics across the industry so far is relatively slow. There is clearly, however, the potential for usage to accelerate. At the macroeconomic level, changes in technology, including AI, could, over time, profoundly affect the nature of the financial services consumed and may result in changes to the structure of the financial services industry. This set of issues is being explored at the Bank of England by Huw Van Steenis in his review of the future of the financial system. What matters to  1  McWaters, J (2018) 2  All speeches are available online at www.bankofengland.co.uk/speeches  2  us as prudential supervisors is the extent to which the development of advanced analytics changes the risks to the safety and soundness of the firms we supervise. Increasing levels of automation, machine learning and AI could improve the safety and soundness of firms in some ways. For example, until recently, most firms were using a rules-based approach to antimoney laundering monitoring. But this is changing and firms are introducing ML software that produces more accurate results, more efficiently2, by bringing together customer data with publicly available information on customers from the internet to detect anomalous flows of funds.3 ML may also improve the quality of credit risk assessments, particularly for high-volume retail lending, for which plenty of data are available and can be used for training machine learning models.