Skip to content

Premium Auditor

Review policy information before audit

Enhances✓ Available Now

What You Do Today

Before contacting the insured, you review the policy, prior audits, classification codes, and estimated exposures to understand what you're auditing and what to look for.

AI That Applies

AI pre-populates audit worksheets with policy data, prior audit results, and flags potential classification issues or exposure changes based on industry trends.

Technologies

How It Works

The system ingests industry trends 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 results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What Changes

You start each audit with a pre-built analysis rather than manually pulling policy documents and prior audit files.

What Stays

Identifying what to focus on during the audit — which classification might be wrong, which exposure likely changed — requires your professional judgment.

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 review policy information before audit, understand your current state.

•
Map your current process: Document how review policy information before audit works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
•
Identify the judgment points: Identifying what to focus on during the audit — which classification might be wrong, which exposure likely changed — requires your professional judgment. 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 Data Aggregation 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 review policy information before audit 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 Chief Compliance Officer

“Which compliance checks are we doing manually that could be continuous and automated?”

They set the risk appetite for AI adoption in regulated processes

your legal counsel

“How would our regulator react to AI-assisted compliance monitoring — have we asked?”

AI in compliance creates new regulatory interpretation questions

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.

← Back to AI for Premium Auditors