— Finance & Investment

Credit / Loan Underwriting

Assesses a credit application against your own credit policy and returns a governed underwriting decision — approve, refer or decline — showing how affordability, risk and policy thresholds drove the outcome, and holding it for a credit officer sign-off before any letter is issued.

  • Finance & Investment
  • Traceable reasoning
  • Runs anywhere
Preview methodology

1–2 hours → ~5 minutes

For one document, brief or record at a time

No coding required

Input
Applicant details, sector and years trading
Output
Approve, Refer or Decline recommendation
Runs in
Skillsize · ChatGPT · Claude · Copilot
Export
SKILL.md · MCP
Time saved
~1–2 hours per run

— USE CASES

What people use Credit / Loan Underwriting for

Routine business loan assessment

Produces a policy-based recommendation supported by debt service cover, leverage and the requested loan’s share of revenue. Makes the financial basis of each assessment explicit.

Applications outside lending appetite

Identifies sector restrictions and mandatory policy conditions that require referral or decline, regardless of the overall assessment. Cites the relevant clauses so exceptions remain visible.

Credit officer review

Provides a structured decision record with a score breakdown, supporting calculations and policy references. Reserves decision-letter authorisation for a credit officer.

Consistency across comparable cases

Uses comparable past decisions to inform the assessment while keeping current credit policy central. Creates a documented precedent for future case comparisons.

What it works from

  • Applicant details, sector and years trading
  • Requested loan amount and term
  • Annual revenue, EBITDA and existing debt
  • Lender credit policy and mandatory decision conditions overlapping with sector appetite and pricing guidance

What you get back

  • Approve, Refer or Decline recommendation
  • Affordability and leverage calculations with supporting arithmetic
  • Assessment breakdown across affordability, leverage, sector risk and trading history
  • Mandatory referral or decline findings with policy clause citations overlapping with the decision rationale

Why this is expensive by hand

Business lending decisions require consistent treatment of financial evidence, sector appetite and policy exceptions. Routine business loan assessment is the typical trigger — produces a policy-based recommendation supported by debt service cover, leverage and the requested loan’s share of revenue. Makes the financial basis of each assessment explicit. Done properly it is defensible; done at pace it becomes a judgement call nobody can retrace. And "properly" usually means 1–2 hours of manual work.

How this Skill produces it

Here the same job runs as a Skill. Your material goes in; approve, Refer or Decline recommendation comes out, alongside affordability and leverage calculations with supporting arithmetic. The judgement is built in — how items are broken up, what standard they are held to, and where the run stops for a human review. Net effect: 1–2 hours down to ~5 minutes, no drift between runs, and every conclusion traceable back to the evidence behind it.

Who it's for

  • Finance directors and FP&A teams
  • Investment, deal and corporate development teams
  • Controllers and reporting managers
  • Consultants building or reviewing business cases

Run it in Skillsize — or export it anywhere

Credit / Loan Underwriting exports as a structured SKILL.md file and is MCP-ready, so the same method runs in ChatGPT, Claude, Copilot or your own AI products. Adapt it to your methodology, and the intelligence stays yours — not locked to one vendor.

ChatGPTClaudeCopilotAI Products (MCP)