FICC Focus Credit Crunch: Where AI Fits With Cognitive Credit’s Rob Slater
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Nov 24, 2025 Rob Slater, CEO of Cognitive Credit, shares insights on automating credit analysis with AI. He discusses how businesses can start low-risk AI experiments to enhance financial data extraction. The conversation dives into the tech gap between equity and credit, emphasizing the need for a tailored credit data strategy. Rob also highlights his journey from banking to founding his firm, the challenges of scaling, and the future of credit teams embracing machine-generated analysis. He's optimistic about the balance between standardization and bespoke deals in the industry.
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Founding Sparked By Equity Tech Gap
- Rob Slater left banking after seeing equity desks cut headcount dramatically and explored machine learning for credit during a year off.
- That led him to team with engineers and found Cognitive Credit to automate credit analytics starting with PDF financial extraction.
PDF-To-Structured-Data Was The Core Problem
- Cognitive Credit's original core problem was reliably machine-reading financial disclosures trapped in PDFs at scale.
- They built a hybrid rules + ML extraction to produce structured, analyst-ready financial datasets and Excel models.
Start AI In Low-Risk Or High-Pain Areas
- Do start AI experiments in low-risk or very high-pain areas of your business to maximize reward while minimizing downside.
- If an experiment fails you lose little, but if it succeeds the upside can be substantial.