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SIU Investigator

Run background and database checks

Enhances✓ Available Now

What You Do Today

You search NICB, ISO ClaimSearch, public records, social media, and internal databases to build a profile on subjects and identify prior claim activity.

AI That Applies

AI aggregates results across multiple databases simultaneously, surfaces hidden connections between claimants, providers, and attorneys, and maps social networks.

Technologies

Link AnalysisSocial Network AnalysisData Aggregation Platforms

How It Works

The system ingests databases simultaneously as its primary data source. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The output — hidden connections between claimants — surfaces in the existing workflow where the practitioner can review and act on it.

What Changes

Database searches that took hours now return consolidated profiles with relationship maps in minutes.

What Stays

You still interpret what the connections mean and decide which leads to pursue — a shared address doesn't automatically equal conspiracy.

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 run background and database checks, understand your current state.

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Map your current process: Document how run background and database checks works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
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Identify the judgment points: You still interpret what the connections mean and decide which leads to pursue — a shared address doesn't automatically equal conspiracy. These are the boundaries AI won't cross.
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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 Link Analysis 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 run background and database checks 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 run background and database checks?”

They're setting the automation strategy for your unit

your SIU lead

“Who on our team has the deepest experience with run background and database checks, 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 run background and database checks, 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.

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