AI consulting use case

AI consulting for underwriting and credit

Underwriting copilots that compress the read on a deal — spreading, memo drafting, and exception-spotting with every figure cited.

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The problem

Why underwriting & credit is hard to get right

Underwriters spend more time gathering and re-keying than judging: spreading financials, chasing documents, and writing memos by hand. The slow part isn't the credit decision, it's the assembly. The challenge is automating that assembly without introducing a model the validation team won't approve or that fair-lending review can't defend.

How we build it

Financial spreading and synthesis

Extraction and normalization of statements and filings into a clean, cited spread the underwriter can verify at a glance.

Memo and exception drafting

Agents that draft the credit memo and flag covenant and policy exceptions, with every figure traced back to its source.

Fair-lending and governance by design

Feature review, documentation, and monitoring built to SR 11-7 and fair-lending expectations, so models survive validation.

Human-in-the-loop decisioning

The copilot prepares; the underwriter decides — with a defensible record of what the model surfaced and why.

The outcome

Credit-memo cycle time drops sharply while the underwriter keeps the decision — with a documented, monitored model your second line can validate and defend.

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Key concepts

GlossaryAI governanceAI governance is the framework of policies, controls, and accountability that decides how an organization builds, approves, monitors, and retires AI systems — so the technology is used responsibly and defensibly.

GlossaryevalsEvals are structured tests that measure how well an AI system performs against a set of real tasks, turning model quality from a feeling into a number you can track.

GlossaryhallucinationA hallucination is when an AI model produces content that is fluent and confident but factually wrong or unsupported by any source.

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Put AI to work on underwriting & credit

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