VP of Lending
Manage credit policy and underwriting standards
What You Do Today
Set and maintain credit policies that define who gets approved, at what terms, and with what conditions. Balance risk appetite with growth objectives and regulatory requirements.
AI That Applies
ML-based credit scoring models that incorporate alternative data sources and non-linear relationships, providing more granular risk segmentation than traditional scorecards.
Technologies
How It Works
The system takes the content brief — topic, audience, constraints, and style guidelines — as its starting input. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The output is a first draft that captures the essential structure and content, ready for human editing and refinement.
What Changes
Credit decisions become more precise. AI approves borrowers that traditional models would decline — and declines borrowers that look good on paper but carry hidden risk.
What Stays
Setting credit policy involves strategic trade-offs between growth, risk, and fairness that require human judgment. The model optimizes within constraints you set.
What To Do Next
This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for manage credit policy and underwriting standards, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long manage credit policy and underwriting standards takes end-to-end today, then after AI adoption.
Why it matters
The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.
Quality of output
How to calculate
Track error rates, rework frequency, or stakeholder satisfaction scores before and after.
Why it matters
Speed without quality is just faster mistakes. Measure both.
Start These Conversations
Who to talk to and what to ask
your board chair or lead independent director
“What content do we produce the most of that follows a repeatable structure?”
They shape expectations for how AI appears in governance
your CTO or CIO
“What's our current review and approval process, and would AI-generated first drafts change the bottleneck?”
They own the technology infrastructure that enables AI adoption
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.