assistance chatbots use natural language processing to provide general explanations, chatbots powered by genai are able to provide a personalised response that is more tailored to the user's situation. genai therefore has the potential to accelerate the adoption of new technologies, and hence the pace of innovation and the transformation of processes : for example, the ability to 1 / 4 bis - central bankers'speeches make computer queries in natural language, and generate code on command, will raise the question of the monopoly of'professional'programmers. more generally, genai could boost productivity : the ai commission, chaired by anne bouverot and philippe aghion, estimated the additional growth that the deployment of ai could generate in france by 2034 at around 1 % per year. supervisors obviously have no intention of remaining on the sidelines of these major transformations, and are already making use of ai technologies to improve their efficiency when performing their duties. other projects already developed at the acpr include the early detection of anomalies in institutions'reporting, or our lucia tool, which analyses large volumes of banking transactions and enables us to assess the performance of the aml / cft models deployed in banks. very recently, with the help of our innovation centre, le lab, the acpr organised a'suptech tech sprint ', a hackathon designed to explore what genai can bring to the various supervisory activities. the three - day event revealed the potential of large language models ( llms ) for supervision, with 8 prototypes jointly developed by external data scientists and acpr staff. 4 projects will be further developed as part of our strategic plan and, i hope, new tools will be created to help the supervisor in many of its activities. this tech sprint also enabled us to lay the groundwork for a longer - term review of the way in which we want to develop supervisory activities : in particular, part of analysis will always have to be carried out by human supervisors, as our main challenge is to maintain a very high degree of reliability in our processes. 2 / because the use of ai is not risk - free, and this is my second point : ai can, in fact, increase risks, not only for individual institutions but also for the financial sector as a whole. firstly, at the microprudential level, i. e. for each individual institution, the use of ai can generate risks for the soundness of the institution and its customers. a poorly calibrated pricing model can

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.