Vehicle Damage Assessment Blackbox

Vehicle Damage Assessment Blackbox


Various fields and industries have taken and have been taking the advantage of artificial intelligence to develop and advance their platforms. Mostly 5 different types of AI like analytics, interactive, text, visual, and functional can add value to any business by sorting down the lead requirements. This article talks about how AI can help in advancing one of the oldest and progressive platform i.e., Insurance Industry. 

Natural language processing, Computer vision, Machine Learning have helped in automating tasks like Customer service, Data extraction, Risk management and many more tasks through which various platforms got benefited. The insurance industry too could benefit from automated tasks but, the main challenge is to identify the appropriate claims to approve.  For a vehicle damage assessment, it is a strenuous task for an inspector to go physically at the spot, investigate the complete accident and identify all the necessary actions to be taken on the claim. 

There are different processes for studying and approving a claim. If an accident occurs, the severity and estimated cost of the repair is to be identified. As discussed earlier it is strenuous to reach the place on time and collect the data and the possibility of errors cannot be evaded.

Our AI integrated Vehicle Damage Assessment Blackbox can investigate the accident and oversee the repair of vehicles without visiting the spot. By identifying and classifying the vehicular damage from images. The severity is marked off on a ranking scale to predict the kind of repair needed and the estimated cost. Both visual and metadata models are applied to achieve the desired outcome. Our blackbox expert tracks and secures the accident investigation data which helps in providing the quick settlement of claims. 




  • Highly reliable method in identifying the severity and estimating the cost.
  • Effort and the time required for collecting data and estimating cost can be reduced.
  • Significant reduction in errors.
  • Reduction in operational cost.

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