I would like to end my comments on the work of the Basel Committee by touching briefly on another regulatory matter currently under review, the treatment of large exposures. This falls within the field of prudential measures additional to the solvency ratio, which aim to ensure the survival of banks in extreme situations. The review focuses on the treatment of exposures to third parties, since that of intra-group exposures has been postponed for the time being. The general principle is that exposures to all counterparties should be subject to limits based on the capital of the bank. That limit has been set generally at 25% of tier 1 capital, although for certain exposures, such as those between systemic banks, it is stricter. Notably, provision is made for the possibility, after an observation period, of limiting exposures to central counterparties (CCPs), although this matter is open and first requires an assessment of the effects of other rules governing relationships between banks and CCPs. Let me now say a few words on some of the main regulatory projects on which the Financial Stability Board (FSB) is currently working, particularly that to promote a new regulatory framework for the treatment of systemically important institutions, i.e. those whose complexity, size and interconnectedness are such that their failure could cause serious problems to the global financial system. This new framework rests on four pillars: additional loss absorbency, effective resolution mechanisms, increased supervisory intensity and more resilient financial market infrastructure. Unquestionable progress has been made in implementing this framework.
Allow me to return to the first topic of the day and say a few considerations about Artificial Intelligence, Machine Learning and Big Data. The interconnected use of new technologies is 1/2  BIS central bankers' speeches  already a common practice in many operational fields, such as the defense industry, the automotive industry or the healthcare sector. It is, therefore, natural that new technologies would penetrate the financial sector. Their applications are diversified in design and scope, examples of which are: algorithmic trading, portfolio optimization, scoring and pricing models. Moreover, the use of these new technologies receives more and more attention in the financial and economic areas, as the transition from classical banking sector products and services is growing exponentially. In this context, the focus moves from the individual to more general issues and the question arises about the effects that the trinity described by artificial intelligence, machine learning and big data analytics, have on the financial system. Implicit answers to this question are formulated as considerations of financial stability. In other words, the key question is related to the costs and benefits that the new technologies listed above have on financial stability. The answers to this question are quite varied, given that this issue has not been thoroughly investigated up to this moment. Among the best-known benefits, some of the areas where progress can be made by using the specified technologies are: regulatory compliance, systemic risk monitoring, fraud detection and supervisory effectiveness.