the acpr, we applied the same principles. take the case of artificial intelligence ( ai ). how do we concretely define the conditions for a controlled development of artificial intelligence and define the principles of its control? it is in this spirit that we designed our ia workshops : the acpr fintech experts and the banque de france data scientists met established players to understand their perception of these technologies and assess their level of maturity. in the areas of aml - cft, internal models and consumer protection, the lessons learned from these meetings and tests under production conditions will provide us with crucial information for building a future framework conducive to controlling the development of artificial intelligence. ( iii ) aware of the importance of the issues and clairvoyant as to their complexity, ready to change our regulation and the exercise of our missions, we must also be bold, that is to say, innovative. to conclude this speech, i would like to stress two important elements. first, european financial sovereignty does not depend solely on the authorities of its member states or on the regulations drawn up by the european commission ; it depends first and foremost on the vitality of the european ecosystem and on the innovation effort of all players, be they established or new. in this regard, the europe of payments should seize the opportunity offered by the digital revolution to develop a pan european payment 3 / 4 bis central bankers'speeches solution. our institutions are an integral component of this european ecosystem and intend to play their full part in it : the banque de france lab, the acpr fintech - innovation unit, with its joint acpr - amf fintech forum, and all public authorities, are fully mobilised to support the expansion and growth of this ecosystem. second, we also apply this injunction to innovate to ourselves : we can only achieve our objectives and truly support the market if we ourselves are innovative. in this respect, our projects are and must remain numerous. i have already mentioned madre, i would like to make a special mention of our analysis of “ weak signals ” by artificial intelligence, to detect fragile companies. this project is remarkable by the cooperation that it involves between a wide variety of public players : the direction generale des entreprises ( dge ), the delegation generale a l ’ emploi et a la formation professionnelle ( dgefp ), the agence centrale des organismes de securite sociale ( acoss ) and the

risks for financial stability, implying a new form of vigilance for central bankers in particular. i will elaborate a bit more on this later on. ii. terabytes of data are available and more will be coming soon. central banks will collect more and more granular data. this is a clear opportunity for better forecasting and even nowcasting. but to reap all the benefits, central banks have to be up to the technological challenge, which is huge. they have also to face a much larger competition in the new data era, with a view to maintaining trust in public information. they have therefore to become big data players and not only observers. this requires significant efforts. let me illustrate this in five avenues to adapt to the digital evolution. a ) the big data era implies large changes in the it infrastructure and a strong determination to address novel technical challenges and to build a forward - looking data management and analysis scheme. at the banque de france, we are building a data lake that covers all the new data functionalities, from data collection to machine learning. b ) central banks should also organise themselves to answer the legitimate request from academics to have access to a wide range of granular data, in a modern and researcher - friendly way. at the banque de france, we opened just one year ago a data room which gives access to 600 million of anonymized series, and it has already attracted more than 30 research teams. harvesting big data capacities is only beginning and central banks should be in the race. c ) data quality should be maintained at the same time. there should not be with central banks input / output process any ‘ garbage in, garbage out ’. managing huge volumes of data, carrying out machine or deep learning requires investing in human and automatic data technology, in training and hiring data analysts and scientists. indeed, big data requires novel and significant human resources, and this has to be anticipated and properly managed. d ) the appetite for real time intelligence, the “ short - termism ” that can be inflated by the big data revolution, entails risks that “ bad data chase good ones ”, thereby impeding the understanding of economic and financial developments by the general public and the media. we must do our best to kill fake news, and there is also economic fake news. but, in a society of communication more than of information, delivering data of good quality, which will always be a trademark of central banks, can hardly be the only answer.