VP of Underwriting
Referral Review & Authority Decisions
What You Do Today
Review and decide on risks that exceed your team's authority — large accounts, complex exposures, out-of-appetite risks that agents are pushing. You're the last line of defense between a bad risk and the balance sheet.
AI That Applies
AI-powered risk scoring that pre-evaluates referred accounts against portfolio guidelines, flags concentration issues, and provides comparable account analysis from historical data.
Technologies
How It Works
The system ingests historical data as its primary data source. Predictive models fit to historical outcome data identify which variables are the strongest leading indicators, then apply those weights to current inputs to generate forward-looking scores. The output — comparable account analysis from historical data — surfaces in the existing workflow where the practitioner can review and act on it. The underwriting decision.
What Changes
Referrals arrive with AI-generated risk assessments, comparable loss experience, and portfolio impact analysis. You focus your judgment on the factors the model can't capture.
What Stays
The underwriting decision. The account that doesn't fit the model but your experience says is good business. The one that scores well but something about the submission feels wrong. That's underwriting instinct.
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 referral review & authority decisions, 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 referral review & authority decisions 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 data do we already have that could improve how we handle referral review & authority decisions?”
They shape expectations for how AI appears in governance
your CTO or CIO
“Who on our team has the deepest experience with referral review & authority decisions, and what tools are they already using?”
They own the technology infrastructure that enables AI adoption
a peer executive at a company further along on AI adoption
“If we brought in AI tools for referral review & authority decisions, what would we measure before and after to know it actually helped?”
Their lessons learned are worth more than any consultant's framework
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.