信贷业务中的人工智能银行：价值创造之路（英）

Transformative technologiesdon’t come along very often, so when they do it pays to actquickly. When gen AI algorithms were launched in 2022, banks wasted little time exploringtheir potential incore commercial credit activities. But three years later, the results are mixed,with some institutions making good progress in putting the technology to work while otherslag behind, a new study from McKinsey and the International Association of Credit PortfolioManagers (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 surveyedsenior executives at 44 financial institutions globally. Across banks ranging in size frommegaplayers to regionals, we asked about the factors affecting their adoption of gen AI, their mostpromising 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, withabout half of senior leaders identifying it as a priority. Indeed, in key applications such ascredit decisioning and pricing, rising numbers of institutions are rolling out one or more usecases. Moreover, credit applications often rank on a par or ahead of other applications, withexecutives seeing particular potential for gen AI in early-warning systems, credit memodrafting, andcustomer engagement activities.That said, sentiment is not universally positive. Many banks are cautious about scaling amidcontinuing skepticism over the technology’s financial benefits. As a result, only a few, mainly largerinstitutions 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 architecturesthey need to implement gen AI successfully. Underlying these challenges, we see two structuralconstraints: First, decision-makers are focused too narrowly on simple use cases rather thanseeking to transform more complex workflows and end-to-end journeys. Second, we find thatmost banks have only recently started to deploy agentic AI, a version of the technology that usesdecisioning algorithms to create cross-cutting impacts, for example, in the middle and frontoffices across lines of business. Banks that address these underlying challenges are creatingcompetitive impetus ahead of their peers.Our methodologyFor the purposesof this article, McKinseysurveyed and interviewed decision-makers at 44 institutions globally in thesecond half of 2024. Our respondentsincluded a roughly equal number ofexecutives across megabanks, super-regionals, and core regionals. Megabankscomprised institutions with more than$1,000 billion in assets, super-regionalsincluded institutions with $500 billion to$1,000 billion in assets, and core regionalswere defined as having $100 billion to$500 billion in assets. We also connectedwith insurance companies/brokers anddevelopment banks. 2Banking on gen AI in the credit business: The route to value creation Most institutions are testing credit use casesGiven a wide range of value creation opportunities, 52 percent of institutions have positionedgen AI adoption as a priority, our survey shows (Exhibit 1). That means senior leadership hasprioritized developing gen AI use cases and backed that ambition through investment and hiring.Another 39 percent of institutions say they are interested in gen AI, but adoption is not yet a clearpriority, and 9 percent admit that senior leaders are not actively engaged on the topic.Exhibit 1Web <2025><AIPortfolio>Exhibit <1> of <5>Leadership commitment to the adoption of gen AI,¹% of respondents1Question: How would you describe your institution’s leadership commitment to the adoption of gen AI? (select one).Source: IACPM and McKinsey study on the use of generative AI in credit portfolio managementLeadership at a majority of institutions positions gen AI as a priority.McKinsey & CompanyMegabankSuper-regionalCore regionalOtherSenior leadership promotes developing gen AIuse cases as a priority and supports throughinvestments and hiring and demonstratesthrough tone and actions that there will besetbacks given the technology is nascentThe organization is encouraged to learnabout gen AI and is supportive of use caseproofs of concept; however, there is lesscommitment to investments or hiring with-out a “proven” ROI and knowledge ofpotential setbacksAdoption is a priorityInterested, but not a clear priorityAdoption is a priorityInterested, but not a clear priority52Commitment to implementation of gen AI, by type of institution,¹number6452 000Senior leadership does notseem to proactively engage withthe topic; the message is ratherto approach with caution basedon the associated risksNot a priorityNot a priority23533 3Banking on gen AI in the credit business: The route to value creation Gen AI offers fi