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Industry — 02

Insurance

Claims is the hardest trade-off between speed and accuracy.

of claims triaged automatically
%60of claims triaged automatically
claim closure time
-%40claim closure time
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Most of the value in insurance sits in two places: accurate pricing and fast claims. Both are full of documents, images and free text — exactly where AI earns its keep. What matters is that an automated decision stays defensible when it is challenged.

In short

What is AI actually good for in insurance?

AI earns its place in insurance in two spots: speeding up the claim file and validating pricing. Documents and photos in a file are turned into structured fields; low-value complete files go to pre-approval and inconsistent ones to an adjuster. Because fraud shows up in the relationship between files rather than in one file, graph analysis is used. Neuros stores every automated decision with its rationale — it has to survive an appeal.

What's hard in this sector

Claims is the hardest trade-off between speed and accuracy.

01

Document density

Policies, adjuster reports, invoices and photos — all unstructured, all feeding the decision.

02

Fraud detection

Organised claims fraud works as a network; rules that look at a single claim never see it.

03

Pricing model risk

Model drift quietly erodes margin. Without drift detection and a retraining discipline, a pricing model decays.

What we can build for you

  • 01Automated claim triage and pre-approval
  • 02Damage assessment and cost estimation from photos
  • 03Organised fraud detection with graph analysis
  • 04An assistant that answers policy and coverage questions
  • 05Risk pricing models with drift monitoring
  • 06Quote automation for the agency channel

Sources

  1. 01Regulation (EU) 2024/1689 — Artificial Intelligence ActAvrupa Birliği Resmî Gazetesi · 2024
  2. 026698 sayılı Kişisel Verilerin Korunması KanunuT.C. Mevzuat Bilgi Sistemi · 2016
  3. 03AI Risk Management Framework (AI RMF 1.0)NIST · 2023

Frequently asked

Questions we get asked

Get in touch

The automation rate depends on the claim amount and document quality, so it cannot be promised as a percentage. Neuros works on thresholds: low-value files with complete documents and a high model confidence go to pre-approval; anything above threshold or showing inconsistency goes to an adjuster. The threshold is tuned against real outcomes over the first months.

Assessment from photos is a first pass, not a replacement for an adjuster's report. The model classifies the location and type of damage, produces an estimated cost range, and hands the file to a human when confidence is low. Neuros always builds these systems with human approval, because image quality, angle and lighting affect accuracy directly.

Degradation is silent: the model keeps running, it just prices wrongly. Neuros monitors two things separately — drift in the input distribution and the gap between predicted and realised loss ratio. When either crosses its threshold an alert fires and retraining is triggered. A pricing model without drift alerting is noticed in the first bad quarter.

Other industries

Let's assess your sector specifically

This page is the general frame. Let's find the actual bottleneck in your operation together, in a 30-minute call.

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