INSURANCE
Fraud Detection
Fraud Detection. What it takes to run it.
Identify recycled photos, metadata tampering, and staged accidents.
// 01 · The question
“Have we seen this damage, this vehicle or this photograph before?”
// 02 · Why it is hard
The other side adapts. Anything that is caught reliably stops being submitted, which means the distribution of what you see is shaped by what your own model already blocks.
// 03 · What the labels have to be
Instance segmentation
Decided before the first frame
The comparison is between damage patterns, and a pattern is a shape. Matching a mask against previously claimed masks is what catches a recycled photograph that has been cropped, rotated or lightly edited.
This is the decision that is expensive to reverse. The geometry has to match what the answer contains, and finding out it does not means labelling the set a second time.
// 04 · How we run it
Three parts of one loop, on this job.
LexAnnotate
Damage Segmentation
Annotate vehicle damage, property defects, and workplace safety conditions with precision bounding boxes and segmentation. Human-in-the-loop verification reduces blind spots in complex edge cases.
LexInsight
Risk Profiling
Link visual damage assessments to claims data, vehicle databases, and property records. Track damage progression over time for workers' comp and commercial property monitoring.
LexAlert
Accuracy Alerts
Monitor deployed claims models as vehicle fleets evolve. Detect accuracy drift when new vehicle types, lighting conditions, or inspection regions change. Auto-retrain before the drift reaches the claims desk.
// 05 · What breaks it after launch
Prior shift is the native failure here. Per-image judgement stays fine while the base rate moves, so the flagged volume and every downstream reserve figure stop making sense before any accuracy metric moves.
It is not the only one that can get this use case, it is the one that usually gets it first. All ten conditions.
// 06 · In insurance
Claims Automation Stability
The Challenge
Damage detection models struggled as vehicle types, lighting, and edge cases increased. Inconsistent visuals caused false positives.
Our Execution
Created production-ready visual datasets. Applied human-in-the-loop validation to reduce blind spots.
The Result
Stabilized accuracy across varied real-world conditions. Reduced manual claim reviews.
6,000+
Cases resolved per month