Artificial intelligence can improve the speed, consistency, and depth of credit analysis — but it can also produce incorrect calculations, unsupported conclusions, and convincing misinformation. Effective use requires a disciplined process for defining the assignment, protecting confidential information, grounding responses in reliable sources, validating results, and applying independent credit judgment. This practical webinar will demonstrate how generative and emerging agentic AI can assist — not replace — credit professionals. Participants will learn to create effective prompts, identify hallucinations, verify calculations and source support, and document human review. Applications will include appraisal, rent roll, cash flow, tenant, market, and refinancing-risk analysis for CRE loans; financial statement, tax return, covenant, borrowing-base, and industry analysis for C&I loans; and working capital, repayment capacity, commodity-price, yield, collateral, and sensitivity analysis for agricultural loans. The program will also address data privacy, fair lending, third-party risk, AI-enabled fraud, output-validation procedures, approved prompt libraries, and the changing responsibilities of lenders, analysts, credit officers, and reviewers.
KEY WEBINAR TAKEAWAYS
- A repeatable AI-assisted workflow to extract, organize, analyze, and validate borrower, collateral, industry, and market information without surrendering professional credit judgment
- Effective prompts that define the source documents, assignment, calculations, assumptions, limitations, output format, and required verification steps
- Appropriate AI use in CRE, C&I, and agricultural credit analysis, including appraisal and rent-roll review, cash-flow analysis, covenant testing, industry research, scenario analysis, credit-memo preparation, and portfolio monitoring
- How to recognize hallucinations and unsupported conclusions while establishing practical controls for confidential information, human review, fair lending, third-party tools, and documentation of AI-assisted work
BONUS MATERIALS
- Library of sample prompts for CRE, C&I, and agricultural credit underwriting, annual reviews, portfolio monitoring, borrower follow-up questions, and credit-memo preparation
- AI Output Validation and Hallucination Detection Checklist for confirming source support, calculations, assumptions, omissions, and contradictory information
- AI Use-Case Risk Assessment and Implementation Checklist covering data classification, approved tools, vendor due diligence, user access, human review, documentation, monitoring, and escalation procedures
WHO SHOULD ATTEND?
- Commercial, CRE, and ag lenders
- Credit analysts and commercial loan underwriters
- Credit administration, senior credit officers, and loan-review personnel
- Chief lending officers, chief credit officers, and enterprise risk managers
- Compliance, information security, IT, and other personnel involved in AI governance or third-party oversight
MEET THE PRESENTER
Robert L. Viering
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