VP of Underwriting
Technology & Process Improvement
What You Do Today
Drive underwriting modernization — straight-through processing, digital quoting, predictive models, and workflow optimization. You're making the case that technology investment improves both speed and quality.
AI That Applies
AI-powered process mining that identifies bottlenecks in the underwriting workflow, and predictive models that enable automated decisions on low-complexity risks.
Technologies
How It Works
For technology & process improvement, the system identifies bottlenecks in the underwriting workflow. Machine learning models identify the patterns in historical data that most strongly predict the target outcome, then apply those patterns to score new inputs. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context. The change management.
What Changes
Simple risks process automatically. The AI handles the personal auto quote while your underwriters focus on the complex commercial accounts that need human judgment.
What Stays
The change management. Getting experienced underwriters to trust AI-assisted decisions, redefining what underwriters do when routine work is automated, and maintaining quality through the transition.
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 technology & process improvement, 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 technology & process improvement 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
“Which steps in this process are fully rule-based with no judgment required?”
They shape expectations for how AI appears in governance
your CTO or CIO
“What's the error rate on the manual version, and what would "good enough" look like from an automated version?”
They own the technology infrastructure that enables AI adoption
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.