Embracing generative AI in credit risk

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Embracing generative AI in credit risk

By Andreas Kremer et al., | McKinsey & Company | July 1, 2024

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Some technologies are so compelling that they quickly take on a life of their own. Generative AI (gen AI) introduced in late 2022 made the leap from the laboratory to the mainstream.  By the first quarter of 2023, big technology companies were integrating gen AI capabilities into their own products and offering programmatic access to generative models for business customers. A year on, gen AI is making its mark in multiple industries, including those that have traditionally taken a relatively conservative approach to the adoption of emerging technologies—credit risk, for example.

McKinsey recently surveyed senior credit risk executives from 24 financial institutions, including nine of the top ten US banks. And asked these executives about their organizations’ adoption of gen AI, its current use cases, their future plans for it, and the challenges they expected.

Twenty percent of the respondents have already implemented at least one gen AI use case in their organizations, and a further 60 percent expect to do so within a year. Even the most cautious of these executives believe that gen AI will be part of their companies’ credit risk processes within two years.

Survey revealed several potential use cases for gen AI in credit risk including in client engagement, during credit decision and underwriting processes, in portfolio monitoring, and in customer assistance processes.

Gen AI has arrived in the credit risk world but has yet to transform it. Executives surveyed were candid about the current state of their gen AI use cases, which are mostly narrow, noncustomer-facing solutions addressing specific operational pain points.

Executives acknowledge that scaling up the application of gen AI in credit risk will be challenging. The most significant barriers, highlighted by 75 percent of our respondents, concern risk and governance. 

To capture the full potential of gen AI in credit risk, financial institutions must move beyond today’s ad hoc approach and develop a common set of practices to prioritize, develop, deploy, maintain, and reuse gen AI applications. Eight such practices are essential: establish an AI road map, aligned processes for building gen AI tools; build a secure, gen AI-ready technology stack; integrate with enterprise-grade foundation models and tools; initiate Robust automated supporting tools; a governance and talent model that can deploy cross-functional expertise to support gen AI development; develop a modular solution architecture; and finally product of these practices should be a library of production-ready, reusable gen AI services and solutions.

3 key takeaways from the article

1. Generative AI (gen AI) introduced in late 2022 made the leap from the laboratory to the mainstream.  By the first quarter of 2023, big technology companies were integrating gen AI capabilities into their own products and offering programmatic access to generative models for business customers. A year on, gen AI is making its mark in multiple industries, including those that have traditionally taken a relatively conservative approach to the adoption of emerging technologies—credit risk, for example.
2. McKinsey recently surveyed senior credit risk executives from 24 financial institutions, including nine of the top ten US banks.  Twenty percent of the respondents have already implemented at least one gen AI use case in their organizations, and a further 60 percent expect to do so within a year. Even the most cautious of these executives believe that gen AI will be part of their companies’ credit risk processes within two years.  Survey revealed several potential use cases for gen AI in credit risk including in client engagement, during credit decision and underwriting processes, in portfolio monitoring, and in customer assistance processes.
3. Gen AI has arrived in the credit risk world but has yet to transform it. Executives surveyed were candid about the current state of their gen AI use cases, which are mostly narrow, noncustomer-facing solutions addressing specific operational pain points.

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Topics:  Technology, Artificial Intelligence, Banking, Financial Markets, Strategy