national financial supervisors - and therefore in france to the acpr, the prudential supervisory and resolution authority. this is a wise choice! it will enable the implementation of this text to be coordinated as closely as possible with existing sectoral rules, with the help - when the time comes - of guidelines from the european supervisory authorities. the acpr stands ready to play the new role that the ai act will entrust to it. it will do so by using a " risk - based " approach, and by making maximum use of existing synergies with prudential supervision, in order to limit the additional burden for financial institutions and for itself. however, i am not minimising the challenge that the implementation of the ai act represents : beyond organisational aspects, we will need to develop an ai audit methodology, which undoubtedly represents a quantum leap in our working methods. we welcome this challenge not as a constraint, but as an opportunity : everywhere in the world, indeed, regardless of whether a specific regulation has been adopted, financial supervisors need to develop new capabilities in order to respond to the growing use of ai by financial actors. and, in fact, we intend to build effective cooperation with other supervisors, particularly at european and international level. 2 / we also want to use ai technologies for our own purposes. we intend to improve our operational efficiency, while giving the banque de france and acpr staff new capabilities. indeed, we want to build a trustworthy ai framework, in which technology complements rather than replaces humans. investment in ai is therefore addressed in a specific action in our strategic plan, monitored at the highest level. this action has two main ambitions : generate a range of generic online services for all our agents - for example with enhanced search 3 / 4 bis - central bankers'speeches capabilities, or document analysis and synthesis functions - and provide support aiagents to address more specific business needs. because learning by doing is the best way to progress, it will start with 5 priority use cases in the short run. to illustrate, let me mention three of them : an internal chatbot assistant to answer employees'questions on hr issues, a tool to detect atypical transactions for aml / ctf purposes - particularly in the french treasury transactions managed by the banque de france - and a structured financial product mapping tool for the acpr. one last point to conclude : the deployment of ai at the banque de france and the acpr is being done in a reasoned and

confined to it staff. ai could thus have a powerful impact on productivity : in france, the ai commission, chaired by anne bouverot and philippe aghion, estimated at around 1 % per year the additional growth that ai deployment could generate in our country over the next ten years. 1 / 4 bis - central bankers'speeches this technological revolution allows many use cases to develop. for example, generative ai - powered chatbots have the ability to provide a personalized response that is more tailored to the user's situation. in insurance, satellite image processing can in some cases be used to directly assess the damage caused to a building, and then automatically manage customer compensation. and this is just the beginning : we are probably far from having explored all the possibilities these technologies can provide. well - controlled ai can therefore increase the efficiency of financial institutions and contribute to enhance their revenues, thereby weighting positively on their profitability - which is a central element of their soundness - including by providing them with risk control solutions. 2 / however, the medal has a flipside, and the power of the solutions developed comes with significant risks. i would like to mention two of them here. first, there is the risk that these technologies may be misused. the complexity and novelty of some modeling approaches can indeed lead to more errors in either design or use of the systems. this poses a risk not only to customers but also to the financial health of institutions, since the wrong calibration of a model could lead to systematic losses. two elements reinforce these risks. first, the real - time adjustment of the parameters of certain models, which is their strength, can also result in rapid drift. second, some ai systems are particularly opaque, giving rise to a " black box " phenomenon. this is of course a customer protection issue, because customers need to be able to understand automated decisions made about them. but it is also a governance issue : an institution that does not have a good understanding of the decisions made by its ai systems cannot claim to control the risks associated with them. i would like to mention a second risk, and not the least important : the cyber risk. in recent years, it has become the number one operational risk in the financial sector. ai can amplify this risk, particularly because technology greatly increases the dangerousness of attackers, such as sneaky code - writing assistants to design malware and synthetic voices to facilitate impersonation. the list of threats is long. this