Insurance & Fintech

Re-architecting claims operations around AI decisioning

A digital-first insurance carrier was drowning in manual claims triage. EIC redesigned the operating model around AI decisioning with human review only where it mattered.

Case study hero image

The results

Faster decisions, lower cost, better fraud capture

71%

Reduction in average claim handling time

9 to 2 days

Time to first decision on routine claims

63%

Of claims auto-adjudicated with human oversight

2.4x

Increase in early fraud signals caught

The challenge

A claims process that scaled headcount, not throughput

The carrier had grown policies faster than its operations could absorb. Every claim flowed through the same manual queue regardless of complexity, and experienced adjusters spent most of their day on low-risk, high-volume cases that needed almost no judgment.

Average claim took 9 business days to reach a first decision, well behind customer expectations.

Adjusters spent roughly 60% of their time on routine, low-dollar claims that required no real judgment.

Fraud signals were caught late because review happened at the end of the pipeline, not the start.

Seasonal volume spikes forced expensive temporary staffing with long ramp-up times.

No structured data captured from decisions, so the process could not learn or improve over time.

Challenge diagram

The approach

AI decisioning with human judgment reserved for the hard cases

We did not bolt a model onto the old queue. We rebuilt the operating model so that automation handled the routine path end to end, and people focused on exceptions, edge cases, and fraud.

Step 01

Map the real decision flow

We instrumented the existing process to find where time actually went and which claim attributes drove complexity, fraud risk, and rework.

Step 02

Segment by complexity and risk

Claims were routed into clear lanes so straightforward cases could be auto-adjudicated while complex and high-risk cases escalated to senior adjusters.

Step 03

Build the decisioning layer

An AI decisioning service scored each claim, drafted a recommended resolution, and surfaced the evidence behind it for fast human verification.

Step 04

Keep humans in the loop

Adjusters reviewed and approved automated recommendations through a purpose-built console, with every override captured as training signal.

Step 05

Measure, govern, and improve

We stood up monitoring for accuracy, drift, and fraud capture so the operation could be tuned safely rather than left to run blind.

Ready to rethink a process that only scales with headcount?

We help operations leaders design AI-native workflows that hold up in production, not just in a demo.

Explore our approach

A clear view of where automation pays off first

Human-in-the-loop design that keeps you in control

A path from pilot to production, not a stalled demo