Confidence gaps in credit risk are fuelling tech investments in European banks

The future of credit risk management in European banking is at a critical juncture. New research from nCino, surveying 220 senior banking executives over 12 major countries, reveals that while 57 per cent of institutions claim to be “advanced” in their digital transformation, a surprising 27 per cent still lack confidence in their ability to manage credit risk effectively. This confidence gap is significant, especially as financial institutions face growing pressure to modernise their credit risk capabilities.

 

Recent years have underscored the fragility of the economy, marked by fluctuating interest rates, bank failures and the global health crisis. These events have eroded banks’ confidence in their ability to manage risk effectively.

 

In response, technology presents one of the solutions: 50 per cent of respondents plan to increase their investments in credit risk technology by up to 5 per cent, while 17 per cent intend to boost spending by 6 to 10 per cent. These figures highlight a clear shift towards technology-driven resilience.

 

A key area of focus is automation and AI. Many institutions are pursuing “intelligent” automation in credit risk management. As one senior credit officer notes, “The ultimate goal is to only investigate clients when there is a problem.” The emergence of generative and predictive AI has showcased the tangible capabilities of AI, spurring significant investment.

 

However, achieving AI-powered automation at scale comes with operational challenges, particularly in data quality and availability, trust and regulatory compliance. “The cleanliness of the data is an absolute minefield,” says a Senior Credit Manager at a Tier-1 European bank. Data challenges are compounded by the need to balance innovation with regulatory compliance, especially with Basel III Endgame on the horizon. Relying solely on AI for credit decisions remains a sensitive issue, raising concerns about transparency and explainability. “We have to make our processes and models more compliant and explainable,” explains a Credit Risk Manager at a European neobank. “There’s always a cutoff between accuracy and explainability.”

 

Adoption levels reflect this caution. Only 17 per cent of respondents report extensive use of AI in their credit risk processes. In the Nordics, for example, institutions prefer models that are interpretable by credit officers, ensuring that human judgment remains integral. This trend aligns with the broader sentiment: 73 per cent of respondents agree that while AI will reduce the number of credit analysts, those remaining will take on more complex, strategic tasks. AI is seen not as a replacement, but as a tool to augment human capabilities.

 

This raises another critical question: how should institutions approach AI implementation? With trust issues surrounding AI models and increasing regulatory scrutiny, financial institutions must carefully navigate this transition. The research reveals a strong preference for external solutions, with 61 per cent of respondents opting for external providers to manage the growing complexity of risk requirements. Only 16 per cent choose to develop solutions in-house, while 23 per cent pursue a hybrid approach, combining both external partnerships and internal development.

 

The complexity of modern credit risk management, coupled with the rapid pace of technological and regulatory changes, appears to make in-house development a more challenging option.

 

European banking’s credit risk management landscape reveals a clear paradox: while most institutions claim digital advancement, nearly a third still lack confidence in their ability to manage risk effectively. This gap is prompting substantial investment in technology, particularly in AI and automation. However, adoption remains cautious, emphasising the importance of human oversight. The future of credit risk management will likely involve a hybrid model, where AI supports, rather than replaces, human expertise. As AI elevates analysts to more strategic roles, the industry is evolving – balancing automated efficiency with human judgment while navigating regulatory challenges and data quality issues.

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