Successful Integration of Generative AI in Banking Requires Both Vision and

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By Nigel Moden, Global and EMEIA Banking and Capital Markets Leader, EY

Across the European banking sector, leaders recognise the potential of generative artificial intelligence’s (GenAI’s) large language models (LLMs) to transform the industry—from customer support and experience to internal operations—and are actively assessing opportunities to realise productivity gains.

Yet, alongside the ambition to deliver truly transformational outcomes for customers, a healthy dose of pragmatism is required to succeed from the outset. Identifying and clearly defining early-phase deployments and ensuring that firms are focusing on use cases that not only build momentum but drive commercial results are key.

Establishing a clear and targeted approach to deploying generative AI (GenAI) in specific banking operations is critical to laying the foundations for a large-scale transformation of the banking industry in the long term.

Where are Europe’s banks on GenAI?

EY’s 2023 “EY European Financial Services AI Survey”—which canvassed the views of executives from 60 financial institutions across the region—evidenced the excitement, optimism and sense of opportunity shared by leaders sector-wide as they assessed the road ahead for artificial intelligence (AI) and GenAI adoption across their organisations.

Nearly two-thirds (61 percent) of European banks told us they had invested in GenAI applications in 2023, and 78 percent stated they planned to increase their spend over the year ahead.

Yet, financial-services executives—banking leaders among them—also highlighted the complex, multidimensional challenges that the sector is already navigating through GenAI adoption: maximising human capital and experience, enhancing business efficiency and mitigating emerging risks.

However, only 52 percent of European banking executives deemed their organisations to be on par with industry peers in their adoptions of AI. Nearly a third (30 percent) believed they were behind the curve, and one in ten (9 percent) had not established any AI-integration plans. This demonstrates the journey yet to be undertaken by the sector just to keep up.

Proactively pragmatic

One of the primary challenges banking leaders face in implementing GenAI is understanding what its true capabilities are. This means banks must build an understanding of AI’s potential applications; determine which ones can unlock real value, momentum and productivity gains; and fine-tune data and applications to achieve the differentiation that will underpin the next phase of GenAI integration across the sector.

The potential use cases of rapidly evolving models are vast and, typically, not built for specific applications; GenAI models require fine-tuning in most, if not all, scenarios.

This fine-tuning process can vary widely in terms of timelines. Some use cases can start delivering value incredibly quickly, while others may require longer phases of development and…