Explore this webinar on AI in credit analysis and agentic AI.

Adam Ahmed (GICP, Fitch Learning) explains how AI tools are being applied across credit workflows, from prompt design to workflow automation, and where their limits still matter. The session is relevant for credit and risk professionals and teams exploring practical AI use in research and analysis.

Key takeaways

• AI in credit is shifting from models to workflow applications: Advances in AI are shifting from rapid model development to how models are integrated into real-world workflows, with growing focus on agentic AI.
• Agentic AI introduces new capabilities and control risks: Unlike chat-based tools, agentic AI systems can execute tasks and iterate towards outcomes, introducing new capabilities, but also new control and security considerations.
• AI adds most value in data-heavy credit analysis workflows such as sourcing, structuring and analyzing unstructured information, rather than replacing rule-based financial models.
• Prompting and context management improve AI output quality: Effective use relies on well-structured prompts and careful management of context to maintain accuracy and relevance.
• Human oversight remains essential in AI-driven credit work: Outputs should be treated as an extension of the user’s work, with responsibility for review and validation remaining with the analyst.
• Agentic AI is reshaping roles in credit analysis: As agentic tools develop, professionals will increasingly need to manage AI workflows alongside traditional analytical responsibilities.

Watch the recording