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

denis beau : the financial supervisor in the age of ai speech by mr denis beau, first deputy governor of the bank of france, at the association europe finances ra©gulations seminar artificial intelligence : a game changer for financial supervision?, paris, 5 june 2024. * * * ladies and gentlemen, it gives me great pleasure to open this seminar on artificial intelligence for financial supervision. the financial industry is data - driven and its analysis is essential, whether for assessing credit risk, estimating the volatility of an asset or the cost of an insurance risk, to name but a few examples. it is therefore only natural that ai technologies, which aim to harness the wealth of data available to financial institutions, should be deployed in this sector. artificial intelligence is already one of the main drivers of the current digital transformation of the financial sector, and the development of genai should further accelerate this trend. from a supervisor's point of view, the impact of artificial intelligence warrants particular vigilance as it is potentially ambivalent : ai is a source of opportunities for the sector, including for its supervisor, but it is also a new vector of risk. this ambivalent impact partly explains the regulatory framework that has just been introduced. in my remarks this morning to introduce your conference, from a supervisor's point of view, i will therefore discuss the opportunities, risks and conditions for an effective regulation of ai for the financial sector. 1 / ai offers many opportunities for the financial sector... and also for supervisors. our observations over the last few years confirm that ai is being used increasingly by financial institutions in all segments of the value chain. they deploy it in particular to improve the'user experience'( for example, chatbots for customer support ), as well as to automate and optimise a number of internal processes. ai is also used to monitor and mitigate risk, as illustrated by its success in use cases relating to the fight against fraud, money laundering and the financing of terrorism ( aml / cft ). properly harnessed, ai is therefore likely to increase the efficiency of financial institutions and contribute to their profitability - which is a key factor in their soundness - including by offering risk control solutions. the arrival of generative ai ( genai ) marks a new phase in this innovation process. the first thing that comes to mind is the qualitative improvement in existing tools : to give a simple example, whereas'' traditional''