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Invite colleaguesGenerative artificial intelligence and large language models for digital banking: First outlook and perspectives
Abstract
After several years of steady progress, the Generative artificial intelligence (AI) and large language models (LLMs, their applications to text) fields have accelerated tremendously since the end of 2022 and the public launch of ChatGPT. This is due to record-breaking model sizes and performances in the last couple of months, triggering unprecedented adoption curves from end users across the world. Even though regulators reacted fast, sharing their first recommendations, auditing emerging players, amending their AI regulation drafts or launching dedicated working groups, these efforts will require several months or years to come to fruition. There are multiple reasons for this. LLMs are complex technological objects made of gigantic foundational models trained on enormous quantities of texts, coupled with dedicated interfaces and action agents. They present a huge potential to perform high varieties of tasks with very high quality but also important risks in terms of costs, content accuracy, transparency, data privacy, security and ethics. Finally, the current ecosystem of stakeholders is very dynamic but also immature. In this uncertain context, the digital banking industry has been reacting ambivalently, with major players banning employee access to ChatGPT and publicly communicating on new LLM initiatives at the same time. This can be explained by the huge potential offered by these technologies to transform their business, coupled with many open questions in terms of technological set-up, usage, compliance and profitability. As these technologies seem to be too transformative for the industry incumbents to just wait and see, they should start creating the right conditions to learn how to use them, by identifying relevant use cases, choosing adapted and simple solutions, designing relevant user experiences, building the right teams, environment, data sets and operating model, and actively engaging in regulatory conversations.
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Author's Biography
Jean-Pierre Sleiman is Head of Digital Operations at N26. For the last 10 years he has been supporting financial institutions and FinTechs in their digital and AI transformation, from strategy definition (building visions and road maps and designing the corresponding enablers) to implementation (delivering AI and data services), as a consultant (Capgemini Invent) and within the industry (BNP Paribas, N26). At N26, Jean-Pierre oversees teams in charge of supporting the company’s Operations department with AI, automation, data and digital solutions. He has been working on many innovative projects to enhance customer experience and foster efficiency, such as Neon, N26’ Customer Service conversational interface, which handles several millions of customer interactions per year in five languages and automates a third of them. Jean-Pierre holds master’s degrees in industrial engineering and innovation management from École des Ponts ParisTech, École Polytechnique and HEC Paris. He has published several papers on innovation management, management of data science teams and AI ethics.