Document Processing · Fraud Detection
ControlExpert needed to process more than 18 million motor claims a year across 30-plus countries, but manual review of vehicle photos, documents, invoices, and fraud indicators slowed down insurers and made consistent claims decisions hard to scale.
Reported impact
ControlExpert turned motor claims into a mostly automated digital workflow that can classify evidence, generate repair-cost estimates, and detect fraud across a global insurance customer base, helping insurers process large claim volumes faster and with more consistent decisions.
ControlExpert built an end-to-end AI claims platform on NVIDIA AI Enterprise running on NVIDIA-Certified Systems with NVIDIA A100 GPUs, NVIDIA Triton Inference Server, PyTorch, and VMware vSphere. The workflow accepts accident notifications, customer-uploaded vehicle photos, and supporting documentation, then uses computer vision to classify images, identify vehicle make, model, color, and license plate, segment visible parts, and estimate repair severity and cost. In parallel, NLP models extract and analyze data from invoices, appraisals, and emails so the platform can recommend payment decisions and flag anomalies. ControlExpert also trained fraud-prevention models that detect manipulated images before a claim moves further downstream. Triton standardizes model serving and lets the insurer-facing pipeline combine image, text, and decisioning tasks in one production stack for insurance carriers around the world.
Customer-submitted photos and documents enter the claims platform, image models classify vehicles and damage, NLP models extract details from invoices and emails, fraud models check for manipulation, and Triton-hosted services return repair descriptions, estimates, and payment recommendations to insurers.
ControlExpert developed AI-based fraud prevention that analyzes and detects manipulated images.
Receiving over 18 million claims a year, ControlExpert needed a solution that could quickly process large amounts of data.
The solution automatically classifies the content of images, such as vehicle photos or documentation.
Using NLP, AI models can extract and analyze data from documentation, including invoices, appraisals, and emails.
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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