Vehicle Damage Assessment for Automotive Insurance Claims
with Image / Video Annotation for Computer Vision
Empowering teams to build world-leading AI products.
Artificial Intelligence (AI) is no longer a buzzword. It’s as mainstream as it gets. From Dating apps to Automotive AI, every tech element has a speck of artificial intelligence in it, & automotive insurance is no different
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
The global automotive collision repair market size was USD 185.98 billion in 2020. It is expected to expand at a CAGR of 2.1% from 2021 to 2028.
The U.S. automotive collision repair market size was valued at USD 33.75 billion in 2018 and is expected to grow at a CAGR of 1.5% from 2019 to 2025
According to Verisk – a data analytics co., USA auto insurers lose $29 bn annually due to errors and omitted information in vehicle damage 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.
Steps to build a robust AI Training Data
To train your Machine Learning Models for Vehicle Damage Assessment, it all starts with sourcing high-quality Training Data, followed up by Data Annotation and Data Segmentation.
Training ML models require a huge set of relevant image/video data. The more the data from different sources, the better would be the model. We work with large car insurance companies that already have numerous images of broken car parts. We can help you collect images and/or videos with a 360° angle from across the globe to train your ML models.
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:
- Damage vs non-damaged
- Damage Side: Front, Rear, Back
- The severity of the damage: Minor, Moderate, Severe
- Damage Classification: Bumper dent, Door dent, Glass shatters, Headlamp Broken, Tail lamp broken, Scratch, Smash, No damage, etc.
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
Car Repair services
by bringing in the required transparency in cost estimation and repairs
Dedicated and trained teams:
- 7000+ collaborators for Data Creation, Labeling & QA
- Credentialed Project Management Team
- Experienced Product Development Team
- Talent Pool Sourcing & Onboarding Team
Highest process efficiency is assured with:
- Robust 6 Sigma Stage-Gate Process
- A dedicated team of 6 Sigma black belts – Key process owners & Quality compliance
- Continuous Improvement & Feedback Loop
The patented platform offers benefits:
- Web-based end-to-end platform
- Impeccable Quality
- Faster TAT
- Seamless Delivery
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
Best-in-class real-life driving data sets
Ready to leverage the power of AI? Get in touch!