Work·AI & Automation·Capability showcase

Insurance claims automation

First-notice-of-loss to first decision in minutes — AI handles the volume cases, humans handle the hard ones, and the audit trail is clean.

The Problem

Insurance claims still take days because every file passes through five hands and three systems. Most cases are routine — they should clear in minutes. The hard 20% deserve actual human time.

The Approach
  1. 01

    Built an FNOL intake on web + mobile that auto-extracts policy, vehicle, location, and parties from photos and free-text.

  2. 02

    Wired vision AI to score photo damage against repair-cost benchmarks for typical claims.

  3. 03

    Implemented a fraud-signal layer — staged accidents, duplicate claims, network analysis — surfaced to adjusters with confidence levels.

  4. 04

    Designed routing: clean low-value claims auto-decide; mid-value and ambiguous claims route to an adjuster with all the context pre-attached.

  5. 05

    Wired the audit trail so every model decision is logged, replayable, and challengeable.

Adjusters should look at twenty hard cases, not two hundred trivial ones.

The Outcome
  • Most routine claims clear before the customer puts the kettle on.

  • Adjusters spend their time on the cases that actually need them.

  • Fraud detection moves from random sampling to consistent signal.

  • Regulator-grade audit is a query, not a quarter-long project.

60–80%
Auto-decision rate (routine)
Minutes
FNOL to decision (auto)
More
Adjuster time on hard cases
On demand
Audit replay
Stack
PythonFastAPIPostgreSQLOCRVision AINext.js

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