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AI estimating uses artificial intelligence to analyze claim information, vehicle images, repair data, or historical patterns and assist with creating or reviewing a collision repair estimate.
Total loss prediction uses available claim, vehicle, damage, and market information to estimate whether repairing a vehicle is likely to be economically impractical under applicable rules.
Automated damage appraisal is the use of software to assess vehicle damage and produce or support an initial evaluation of repair needs, severity, or cost.
AI bias is a systematic tendency for a model’s results to favor, disadvantage, or misrepresent certain situations because of its training data, design choices, or how it is used.
Claims triage is the process of sorting new claims by factors such as severity, complexity, repairability, urgency, and the type of review they require.
A confidence score is a number that represents how certain an AI system is about a particular prediction or classification. It is not a guarantee that the result is correct.
Damage detection is the use of image analysis or other sensors to locate and classify visible vehicle damage such as dents, scratches, cracks, and broken components.
Data privacy concerns how personal, vehicle, claim, image, and business information is collected, used, shared, retained, and protected.
Explainable AI refers to methods that help people understand why an artificial intelligence system produced a particular result or recommendation.
First notice of loss is the initial report to an insurer that an accident, damage event, or other covered loss has occurred.