AI in Credit Analysis: Best Practices, Prompt Patterns, and Agentic Applications

In this webinar, Adam Ahmed (GICP, Fitch Learning) discusses how AI is being applied in credit, with a focus on prompt patterns, context management, agentic AI and the practical considerations that matter in real workflows. The session explores what large language models can and cannot do, where AI can add value in credit processes, and why responsible adoption depends as much on judgment, governance and workflow design as it does on the technology itself.

Key themes

• Understanding the difference between AI tools and agents
  The session distinguishes between conversational AI tools and agentic systems that can execute tasks within defined constraints, highlighting why this difference matters in workplace applications.
• Using AI where it adds the most value
  Rather than replacing deterministic credit logic, AI is presented as most useful in areas such as data acquisition, parsing, preparation, extraction and workflow support.
• The importance of context management
  The webinar emphasizes that effective use of AI depends not only on prompts, but on how context is curated, structured and managed over time.
• Prompt design as a learning tool
  Good prompts are framed to support analysis, challenge assumptions and encourage learning, rather than simply asking AI to provide an answer.
• Managing risk and accountability
  The session highlights the need for human ownership, especially where hallucinations, security concerns and workflow risks may affect outputs.
• Preparing for changing roles
  As agentic tools develop, credit professionals may need to think more like managers, prioritizing tasks, structuring workflows and deciding how AI should support their work.

Watch the webinar recording to explore how AI can support credit workflows, what agentic applications may look like in practice, and why prompt and context design matter for effective use.