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SIU Investigator

Prepare investigation reports and case summaries

Enhances✓ Available Now

What You Do Today

You compile findings into structured reports for claims management, legal counsel, or law enforcement referral — organizing evidence, timelines, and witness statements.

AI That Applies

AI drafts report templates from your case notes and evidence log, organizing findings chronologically and flagging gaps in documentation.

Technologies

How It Works

The system ingests case notes and evidence log as its primary data source. NLP models process the text input by identifying entities, classifying intent, and extracting the structured information needed for downstream decisions. The output is a structured view that highlights exceptions, trends, and items requiring attention — available in the existing tools without switching systems.

What Changes

Report drafting time drops significantly when AI structures your raw notes into a formatted investigation summary.

What Stays

Your professional opinion, conclusions of fact, and recommendations for disposition — those require your expertise and credibility.

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 prepare investigation reports and case summaries, understand your current state.

Map your current process: Document how prepare investigation reports and case summaries works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Your professional opinion, conclusions of fact, and recommendations for disposition — those require your expertise and credibility. 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 Document Generation 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 prepare investigation reports and case summaries 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 claims director or VP Claims

Which of our current reports are manually assembled, and how much time does that take each cycle?

They're setting the automation strategy for your unit

your SIU lead

What questions do stakeholders actually ask that our current reporting doesn't answer?

AI fraud detection changes how investigations are triggered and prioritized

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.