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SIU Investigator

Conduct field surveillance

Automates◐ 1–3 years

What You Do Today

You stake out claimants, photograph activity inconsistent with claimed injuries, and document findings for potential denial or prosecution referral.

AI That Applies

AI-assisted video analysis can timestamp and tag activity in surveillance footage, and geolocation tools help plan optimal surveillance positions.

Technologies

How It Works

For conduct field surveillance, the system draws on the relevant operational data and applies the appropriate analytical models. Computer vision models analyze the visual input by detecting objects, measuring spatial relationships, and comparing against trained reference patterns to identify matches or anomalies. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What Changes

Reviewing hours of surveillance footage gets faster when AI can flag movement and activity automatically.

What Stays

Being in the field, making real-time decisions about when to follow and when to pull back — that's still you.

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 field surveillance, understand your current state.

Map your current process: Document how conduct field surveillance works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Being in the field, making real-time decisions about when to follow and when to pull back — that's still you. 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 Computer Vision 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 field surveillance 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 field surveillance?

They're setting the automation strategy for your unit

your SIU lead

Who on our team has the deepest experience with conduct field surveillance, 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 field surveillance, 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.