Generative artificial intelligence and large language models for digital banking: First outlook and perspectives

Jean-Pierre Sleiman
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Jean-Pierre Sleiman: N26 Operations GmbH, Germany

Journal of Digital Banking, 2023, vol. 8, issue 2, 102-117

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.

Keywords: artificial intelligence; AI; Generative AI; large language models; LLMs; machine learning; digital banking; innovation (search for similar items in EconPapers)
JEL-codes: E5 G2 (search for similar items in EconPapers)
Date: 2023
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