Transportation, legal tech & insurance
Government & insurance sector
Accident analysis and liability assessment
Measured results
−70%
manual investigation time
hours
to process a claim, from weeks
Source: Epsilon AI Success Stories & Project Highlights, Ver. 3 — 15 October 2025, page 10
The problem
Manual accident investigation was slow and inconsistent between investigators, with no standard basis for attributing fault.
What was built
A mobile-enabled system that validates the vehicles present, classifies damage, and applies liability rules to produce a report at the scene.
Technical approach
- Vehicle validation
- YOLOv8 detection confirming which vehicles are actually in frame.
- Damage assessment
- Deep learning classification of damage type and severity.
- Liability analysis
- Spatial analytics combined with the applicable legal and insurance rules.
Measurement framework
Deployment evidence is drawn from the published engagement. The delivery and measurement plans are case-specific recommendations grounded in the documented solution and results.
- Evidence status
- The source documents a field-to-claim accident-assessment workflow, supported by reported reductions in investigation and processing time.
- Indicative delivery plan
- Define the evidence schema and liability workflow, validate outputs against adjudicated cases, conduct a supervised field pilot, then introduce a governed production rollout with human review.
- Recommended baseline
- Recommended: median investigator handling time and end-to-end claim cycle time for closed pre-deployment cases, segmented by case complexity and evidence volume.
- Recommended measurement method
- Compare matched case cohorts, timestamp each workflow stage and exclude cases awaiting external evidence. Quality-assure a blinded sample against the final adjudicated outcome; report time savings separately from decision accuracy.
Recommended items are not presented as the original study method. They should be replaced with the confirmed project methodology when the underlying records are approved for publication.