Insurance · Damage assessment vision
Banao builds computer-vision damage assessment that scores motor and property claims from photos at intake — severity category, repair estimate range, and likely total-loss flag — before a surveyor has been dispatched.
The model runs against the photos adjusters already collect at FNOL, wiring its output into your claims system so the first reserve is set in hours, not the days a field calendar takes.
A multi-line general insurer— vision damage scoring on motor intake photos, wired into the claims platform.
A vision deployment for claims is the model, the integration with your claims platform, and the adjuster workflow around it — we own all three.
The model reads photos submitted at FNOL and returns a damage severity category, estimated repair cost band, and part-level damage flags — all before the first adjuster touches the file.
Structural, weather, and fire damage across roof, walls, and contents — classified from insured-submitted or desktop-survey images into categories your loss-estimating tool understands.
The model identifies claims that are likely total-loss candidates early, so salvage decisions are made at intake rather than after a surveyor has spent two hours on a vehicle that will be written off.
Vision models that spot inconsistencies between damage photos and claimed incident details — pre-existing damage, staging indicators, metadata anomalies — and pass a signal to your fraud detection workflow.
Model output is not a black-box number. Each assessment includes annotated images, confidence scores, and the specific damage elements that drove the estimate — so the adjuster can review, override, and move on.
Output maps directly into your reserve fields, case notes, and task queues — whether you are on Guidewire, Duck Creek, or a bespoke platform. The integration is part of the build deliverable.
Named insurers are under NDA; receipts are described by line of business. Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.
A multi-line general insurer
FNOL intake photos were already being collected but not analysed. Banao trained a motor damage model on the insurer's own claims image library, wired output into the core claims platform, and put annotated assessments in front of adjusters at first touch.
Banao runs a ~300-person engineering company on its own AI systems. InterviewGod screens every engineering hire we make; Vikaas runs our demand generation. A model that has to survive our own operation is already stress-tested before it reaches your claims floor.
That is the standard we apply to insurance AI: built and operated against live data, not a pilot that was never meant to go to production.
Not every claims operation is ready for a vision model. We will say so before you spend:
We do not scope a vision deployment off photos we have never seen. We test your actual claims images first.
We audit a sample of your intake photos, test the model on your hardest damage classes, and hand back a baseline accuracy estimate, a coverage map by claim type, and the ROI maths — yours to keep regardless. If you proceed, the Sprint fee is credited against the build.
Label, train to your damage categories and reserve bands, and integrate with your claims platform, FNOL workflow, and reserve fields. Image pre-processing and the data pipeline are part of the deliverable.
Live deployment with adjuster override and a management dashboard showing accuracy trends by claim type, line, and image quality. Adjuster corrections feed back into the model each week.
What photo quality do you need from FNOL intake?
Sufficient resolution and coverage to read the damage — typically eight to twelve photos per vehicle, or multiple angles for property. The Discovery Sprint establishes whether your current intake photos meet the bar or whether you need to adjust your FNOL capture guidance.
Does the model work for both motor and property claims?
Yes, but as separate deployments trained on separate labelled datasets. Motor damage patterns are structurally different from property damage — the same model does not serve both lines. We scope and build them independently.
How does output wire into our claims system?
The model returns structured output — severity category, estimate band, flags, annotated images — that maps into your reserve fields, task queues, and case notes. We have built integrations on Guidewire, Duck Creek, and bespoke platforms; the integration scope is agreed during the Sprint.
Will this replace our field surveyors?
For a fraction of claims, yes — the model identifies likely total-loss vehicles or straightforward property losses that can settle without a site visit. For the majority, it changes the surveyor's role: they arrive with a pre-scored file and focus their time on the cases that genuinely need them.
How do adjusters override the model when it is wrong?
Every assessment includes the confidence score and the damage elements that drove it. Adjusters can override any call in your existing claims UI; those corrections are batched back into the model's training pipeline weekly. The override rate is tracked on the management dashboard.
Send us a sample of your toughest motor or property claims photos. In 45 minutes we will show you what the model returns — and whether the accuracy justifies building it.
Book a 45-min scoping call
