. take artificial intelligence, for example. this has been used by banks for credit scoring, and by insurers for underwriting and fraud detection. supervisors, including mas, have taken steps to ensure that artificial intelligence ( ai ) is used by financial institutions in a fair and responsible manner. predictive analytics can also empower supervisors to be more effective. ai and machine learning, when applied to the myriad of data that supervisors have, offer the possibility of real - time customised risk assessments and early warning capabilities. this can inform policy interventions in times of heightened risk, and help to preserve financial stability. to this end, the bis innovation hub singapore centre, is spearheading a regtech and suptech project to pilot the development of an integrated regulatory reporting platform and data analytics utility. this allows supervisors to assess microprudential and macroprudential risks by applying ai and machine learning tools to unstructured data as well as to big datasets. the application of such tools is enabled by transforming regulatory reporting to be based on the mapping of granular data to a common data model, moving away from fixed templates. this integrated solution, which has the potential to be scaled across supervisory authorities, will be a tangible example of a global public good that the bis innovation hub can deliver for the benefit of the central banking and supervisory communities. leading innovation 6. having touched on the ‘ why ’ of innovation, let me share some views on ‘ how ’ central banks can support and facilitate innovation by the industry. the central banking community has already been generally facilitative towards innovation while managing the inherent risks. in fact, some central banks including mas have deliberately provided room for emerging technologies to be explored deeply, and their benefits and risks to be properly understood. we have avoided having regulation front - run innovation, so as not to stifle new ideas. beyond experimentation, such exploration may also require testing of use cases in the market to assess their technical functionality and commercial viability in a production environment. to this end, regulatory sandboxes have come in useful over the past few years. tailored relaxation of regulatory requirements within well - defined parameters has allowed opportunities afforded by new technologies to be explored, while maintaining the overall soundness of the financial system. but central banks ’ role in innovation goes further than this.. moving on to my second point ; beyond embracing and facilitating innovation ; central banks can play an important role in leading innovation, by being at the centre of experimentation.

the growing size of these institutions, their spread across geographic boundaries, and their diversity of products and customers, have made it necessary for decision - making to be decentralized, at least to some extent, in order to be responsive to the needs of customers. but with more autonomous units, an internationally - active and complex bank has to expend more effort to identify, measure, and control the risks arising from its diverse and far - flung activities. traditional rules of thumb used by management and supervisors to assess risks and to ensure the adequacy of capital to buffer risks are no longer adequate. and continuing financial success depends critically on how a bank responds to the risk management challenges. in response to these challenges, the successful banks have developed enhanced tools for measuring risks. these can be grouped into two broad categories. the first category of tools are those that give a bank the ability to discriminate among risks by differentiating the riskiness of customers, and of various banking facilities and products. the second category of tools includes those that give a bank an enhanced capability to quantify risks. the end of the road for basel i in and of itself, the enhanced capability of institutions to differentiate and measure risks has been a welcome development. but, the increasing sophistication of institutions in risk management meant that the crude system of risk weights under basel i became less meaningful in ensuring the adequate capitalisation of large, complex banking institutions. by the mid - 1990s, less than five years from the official implementation of basel i, 1 it was apparent that basel i was becoming less relevant for some banking institutions. a widening gap was opening up between the requirements of basel i and the way in which banks, particularly the large, internationally active banks, and some of their unregulated competitors, were thinking about risks. this placed banks at a competitive disadvantage vis - a - vis some of those unregulated competitors, and was leading to extensive capital arbitrage by banks. the sophisticated approaches under basel ii were devised as a solution to this problem. the underlying philosophy of basel ii was simple and sensible : ( i ) where possible, rely on the risk management practices of banks as the basis for setting more risk sensitive capital requirements. ( ii ) provide incentives to banks to continuously improve their risk management practices. as this idea began to take shape, and the basel committee began to fall in love with it, we persuaded ourselves that it would lead to a supervisory nirvana, at least for the well