麦肯锡-信贷业务中的人工智能银行：价值创造之路（英）

July 2025Risk & Resilience PracticeBanking on gen AI in the credit business: The route to value creationBanks have taken steps to accelerate the adoption of gen AI in the credit business, but most remain on a long-term journey, according to a recent survey.This article is a collaborative effort by Arvind Govindarajan, Filippo Maggi, and Kevin Buehler, with Jania Kesarwani and Maria Acuna, representing views from McKinsey’s Risk & Resilience Practice.Transformative technologies don’t come along very often, so when they do it pays to act quickly. When gen AI algorithms were launched in 2022, banks wasted little time exploring their potential in core commercial credit activities. But three years later, the results are mixed, with some institutions making good progress in putting the technology to work while others lag behind, a new study from McKinsey and the International Association of Credit Portfolio Managers (IACPM) shows (see sidebar, “Our methodology”).Gen AI is now a priority for many banksTo gauge banks’ progress in adopting gen AI in the credit business, we interviewed and surveyed senior executives at 44 financial institutions globally. Across banks ranging in size from megaplayers to regionals, we asked about the factors affecting their adoption of gen AI, their most promising use cases, and their approaches to managing risks associated with the technology.The responses were unequivocal on one point: Gen AI is starting to break through, with about half of senior leaders identifying it as a priority. Indeed, in key applications such as credit decisioning and pricing, rising numbers of institutions are rolling out one or more use cases. Moreover, credit applications often rank on a par or ahead of other applications, with executives seeing particular potential for gen AI in early-warning systems, credit memo drafting, and customer engagement activities.That said, sentiment is not universally positive. Many banks are cautious about scaling amid continuing skepticism over the technology’s financial benefits. As a result, only a few, mainly larger institutions are ahead of the curve, while most say progress has been slower than expected.Survey respondents tell us there are several reasons for the industry’s incrementalist approach. Many banks, for example, are still missing the skills, frameworks, and operational architectures they need to implement gen AI successfully. Underlying these challenges, we see two structural constraints: First, decision-makers are focused too narrowly on simple use cases rather than seeking to transform more complex workflows and end-to-end journeys. Second, we find that most banks have only recently started to deploy agentic AI, a version of the technology that uses decisioning algorithms to create cross-cutting impacts, for example, in the middle and front offices across lines of business. Banks that address these underlying challenges are creating competitive impetus ahead of their peers.Our methodologyFor the