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Director of Special Investigations

Coordinate with claims operations on referral processes

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What You Do Today

Manage the interface between claims operations and SIU. Ensure referral processes are efficient, feedback loops exist, and claims staff understand when and how to involve SIU.

AI That Applies

Automated referral scoring that prioritizes incoming referrals and provides feedback to referring adjusters on referral quality.

Technologies

How It Works

For coordinate with claims operations on referral processes, the system draws on the relevant operational data and applies the appropriate analytical models. The automation engine executes each step in the process sequence — validating inputs, applying business rules, generating outputs, and routing exceptions to human review queues. The output — feedback to referring adjusters on referral quality — surfaces in the existing workflow where the practitioner can review and act on it. The working relationship between SIU and claims is delicate.

What Changes

Referral quality improves with automated feedback. Adjusters learn which referrals were productive and which were false alarms.

What Stays

The working relationship between SIU and claims is delicate. Claims staff need to trust SIU as partners, not adversaries.

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 coordinate with claims operations on referral processes, understand your current state.

Map your current process: Document how coordinate with claims operations on referral processes 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 working relationship between SIU and claims is delicate. 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 workflow tools 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 coordinate with claims operations on referral processes 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 steps in this process are fully rule-based with no judgment required?

They're setting the automation strategy for your unit

your SIU lead

What's the error rate on the manual version, and what would "good enough" look like from an automated version?

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.