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VP of Underwriting

Reinsurance Coordination

Enhances◐ 1–3 years

What You Do Today

Coordinate with the reinsurance team on treaty capacity, facultative placements, and how underwriting strategy aligns with reinsurance program design.

AI That Applies

AI that models how underwriting decisions affect reinsurance treaty performance, optimizes cession strategies, and flags accounts that require facultative placement before binding.

Technologies

How It Works

For reinsurance coordination, the system draws on the relevant operational data and applies the appropriate analytical models. The simulation engine runs thousands of scenarios by varying each uncertain input across its probability range, building a distribution of outcomes that quantifies the risk. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context. The reinsurer relationships and strategic alignment.

What Changes

Reinsurance impact analysis happens at the point of underwriting. The AI shows how binding this account affects treaty loss ratios and whether facultative placement is needed.

What Stays

The reinsurer relationships and strategic alignment. How you position your underwriting strategy in the reinsurance market affects your cost and capacity for years.

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.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for reinsurance coordination, understand your current state.

Map your current process: Document how reinsurance coordination works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: The reinsurer relationships and strategic alignment. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support Simulation tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long reinsurance coordination 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.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

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 reinsurance coordination?

They shape expectations for how AI appears in governance

your CTO or CIO

Who on our team has the deepest experience with reinsurance coordination, 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 reinsurance coordination, what would we measure before and after to know it actually helped?

Their lessons learned are worth more than any consultant's framework

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.