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SIU Investigator

Conduct recorded statements

Enhances◐ 1–3 years

What You Do Today

You interview claimants, witnesses, and providers — asking probing questions, reading body language, and documenting inconsistencies in their narratives.

AI That Applies

AI transcription captures statements in real time, and sentiment analysis can flag vocal stress patterns or narrative inconsistencies during or after interviews.

Technologies

How It Works

For conduct recorded statements, the system draws on the relevant operational data and applies the appropriate analytical models. NLP models process the text input by identifying entities, classifying intent, and extracting the structured information needed for downstream decisions. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What Changes

You get instant transcripts with highlighted inconsistencies rather than reviewing hours of recordings manually.

What Stays

The interview itself — reading people, building rapport to get admissions, and adapting your questioning in real time is irreplaceably human.

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 conduct recorded statements, understand your current state.

Map your current process: Document how conduct recorded statements 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 interview itself — reading people, building rapport to get admissions, and adapting your questioning in real time is irreplaceably human. 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 Speech-to-Text Transcription 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 conduct recorded statements 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

What data do we already have that could improve how we handle conduct recorded statements?

They're setting the automation strategy for your unit

your SIU lead

Who on our team has the deepest experience with conduct recorded statements, and what tools are they already using?

AI fraud detection changes how investigations are triggered and prioritized

a claims adjuster with 15+ years experience

If we brought in AI tools for conduct recorded statements, what would we measure before and after to know it actually helped?

Their judgment sets the benchmark that AI tools are measured against

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.