- What We Do
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- Off-the-shelf Data
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Empowering teams to build world-leading AI products.
AI in automotive insurance holds significant potential to quickly estimate vehicle damages. Soon with the advancement in AI algorithms, assessment done manually would be a thing of the past. Traditionally the damage assessment was carried out by multiple parties which were time-consuming, highly prone to human error, leading to inaccurate cost estimations
According to Verisk – a data analytics co., USA auto insurers lose $29 bn annually due to errors and omitted information in vehicle damage detection and assessment
Machine Learning has seen widespread adoption when it comes to automating repetitive manual processes. With next-gen technology, algorithms, and frameworks, AI can understand the process of identifying and recognizing damaged parts, assessing the extent of damage, predicting the kind of repair needed, and estimating the total cost. This can be achieved with the help of Image/Video Annotation for Computer vision to train ML models. The ML models can extract, analyze, and offer insights that result in a quick inspection process that takes into consideration the road, weather, lighting, speed, damage type, accident severity, and traffic with greater accuracy.
To train your Machine Learning Models for Vehicle Damage Detection and Assessment, it all starts with sourcing high-quality Training Data, followed up by Data Annotation and Data Segmentation.
License off-the-shelf Vehicle image dataset/Car image dataset to train machine learning models to accurately assess vehicle damage, so as to predict insurance claims while minimizing loss for the insurance companies.
Once the data is collected the system should automatically identify and analyze objects and scenarios to assess the damages in the real world. This is where data annotators help you annotate thousands of images/videos which further can be used to train ML models.
The annotators can help you annotate a dent, ding, or crack from the outer/inner panels of the car which includes: bumpers, fenders, quarter panels, doors, hoods, engine, seats, storage, trunks, etc.
Once the data is annotated the same can be segmented or classified as:
55k annotated images (1000 per model) of 2-wheelers along with metadata.
82k annotated images (1000 per model) of 3-wheelers along with metadata
32k annotated images (along with metadata) of
damaged 4 wheelers.
5.5k videos of cars with minor damages from India and North America regions
An ML model built on high-quality data from Shaip can help
that build Machine Learning Models for Automobile Insurance
by preventing frauds and speeding up the underwriting process
by bringing in the required transparency in cost estimation and repairs
by bringing transparency between customer and rental company while renting a car
Dedicated and trained teams:
Highest process efficiency is assured with:
The patented platform offers benefits:
Dedicated and trained teams:
Highest process efficiency is assured with:
The patented platform offers benefits:
Managed workforce for complete control, reliability & productivity
A powerful platform that supports different types of annotations
Minimum 95% accuracy ensured for superior quality
Global projects across 60+ countries
Enterprise-grade SLAs
Best-in-class real-life driving data sets
Ready to leverage the power of AI? Get in touch!