Redefining credit risk in banking

We recently hosted an insightful webinar, Redefining Credit Risk in Banking, bringing together senior leaders from KeyBank, IFC and Bank of Hope to discuss how banks are rethinking credit operating models and leveraging artificial intelligence (AI) and data-driven insights to strengthen credit decision-making.

Key takeaways

While credit fundamentals remain unchanged, technology is transforming execution

The core principles of credit underwriting and risk assessment remain unchanged. However, the ability to access more data, analyse it rapidly and make decisions more efficiently through advanced analytics and AI is redefining how credit risk is managed. Technology is enhancing, not replacing, sound credit judgement.

Data quality and governance are the foundation of AI success

AI is only as effective as the data on which it is built. Trusted, connected and well-governed data is critical for generating reliable insights. Organisations therefore need clear data ownership, robust quality controls and strong governance frameworks before scaling AI initiatives.

AI adoption succeeds when tied to real business problems

The most successful AI use cases are not necessarily the most sophisticated. AI solutions scale when they address a specific business need, integrate seamlessly into existing workflows and deliver measurable outcomes. Equally important is building trust in AI-generated outputs, which is essential for user adoption and long-term success.

Human judgment remains indispensable in credit risk management

The panellists emphasised that AI should augment, not replace, credit professionals. Human oversight is essential to interpret outputs, challenge assumptions, understand changing market conditions and make final credit decisions. Moreover, accountability and business expertise must remain embedded throughout the credit process.

AI's greatest near-term value lies in efficiency and continuous monitoring

According to the panellists, AI is enabling a shift from periodic reviews to near real-time risk surveillance. Its practical applications include:

• Policy and procedure searches
• KYC and due diligence processes
• Document review and keyword extraction
• Financial spreading and analytical support
• Continuous portfolio monitoring and early-warning detection

Scaling AI requires organisation-wide alignment, not just technology

Successful AI adoption does not depend on technology alone. While technology is a critical enabler, success ultimately hinges on operating models, governance, people and cross-functional collaboration. Business teams, risk managers, technology teams and data specialists must work together from the outset. Institutions should generally buy for scale and innovation while retaining ownership of their data, governance frameworks, risk controls and business judgement.

The bottom line

The overarching message from the panel was clear: the future of credit risk management lies in combining trusted data, AI-powered insights and human expertise to enable faster, more proactive and better-informed decision-making while maintaining strong credit discipline.