How This Insurtech Solving Damage Detection Problem With AI

Tractable using computer vision to detect damage
Tractable using computer vision to detect damage

Vehicle damage detection refers to the process of using various technologies, such as computer vision and machine learning, to identify and classify damage on vehicles. This can include damage caused by accidents, vandalism, or wear and tear.

Vehicle damage detection is becoming increasingly important in several industries, including insurance, automotive manufacturing, and car rental companies. For example, insurance companies need to accurately assess the extent of damage to a vehicle to process a claim, while automotive manufacturers can use damage detection to identify potential quality control issues on the assembly line.

Various techniques can be used for vehicle damage detection, including image classification, object detection, and anomaly detection. These techniques involve training algorithms on large datasets of images of damaged and undamaged vehicles to identify patterns and features that distinguish between them. The algorithms can then be used to analyze new images of vehicles and classify any damage present.

Overall, vehicle damage detection has the potential to streamline processes and reduce costs for businesses while also providing more accurate and reliable assessments of damage.

damaged car

The problem of vehicle damage is significant and can result in the loss of billions of dollars each year. For example, the National Highway Traffic Safety Administration (NHTSA) reports that in 2019, there were approximately 6.7 million police-reported crashes in the United States alone, resulting in over 36,000 fatalities and more than 2.7 million injuries. These crashes are estimated to cost around $836 billion per year.

Cost Analysis on using automated vehicle detection systems

Not only automated vehicle detection systems work more efficiently, but it is also cost wise efficient and reduces the cost to the company which provides the service. To support this argument:

  1. According to a report by Frost & Sullivan, automated vehicle detection systems can result in significant cost savings over manual systems. The report estimates that automated systems can reduce labor costs by up to 80%, resulting in potential savings of up to $2,000 per vehicle per year.
  2. A case study by SmartDrive Systems provides an example of the cost savings that can be achieved with an automated vehicle detection system. The study found that a large transportation company reduced its accident frequency by 70% and its accident costs by 55% after implementing an automated system. The company estimated that the system paid for itself in six months due to the savings achieved.

Computer vision in vehicle detection

Computer vision plays a critical role in vehicle damage detection by quickly and accurately identifying damage and defects.

For instance, computer vision algorithms can be used in the insurance industry to assess the extent of vehicle damage after an accident, allowing for faster claims processing and reduced costs. Computer vision can also be used to detect and quantify damage during the vehicle inspection process, ensuring that damaged vehicles are identified and repaired before being put back on the road.

In addition, computer vision can be used to monitor the condition of vehicles and identify any issues that may require maintenance or repair, helping to prevent accidents and reduce costs. For example, fleet operators can use computer vision to track the wear and tear of their vehicles, allowing them to schedule maintenance and repairs proactively. Overall, computer vision has become a crucial technology in the field of vehicle damage detection, providing significant benefits in terms of cost savings, increased efficiency, and improved safety.

Computer Vision Vehicle Detection

One such company which utilizes computer vision to solve the problem of vehicle damage detection is Tractable.

About Tractable

Tractable is an artificial intelligence company specializing in automating visual inspections using computer vision technology. The company was founded in 2014 and is headquartered in London, UK, with additional offices in Tokyo and New York.

Tractable's main product is an AI-powered solution for automating vehicle damage assessments. The solution uses computer vision algorithms to analyze images of damaged vehicles and provide an estimate of the repair costs. Leading insurers use this technology worldwide to speed up the claims process and reduce costs.

In addition to its vehicle damage assessment solution, Tractable offers various computer vision solutions for the construction, utilities, and agriculture industries. These solutions use AI to automate inspections, detect defects and anomalies, and improve operational efficiency.


Tractable has received multiple awards and recognition for its innovative technology, including being named one of the World Economic Forum's Technology Pioneers and being recognized by CB Insights as one of the 100 most promising AI startups. The company has also raised significant funding from investors such as Georgian, Insight, and Ignition Partners.


The CEO of Tractable is Alex Dalyac. Dalyac co-founded the company in 2014 after completing a Ph.D. in computer science at Imperial College London. Before starting Tractable, he worked as a research assistant at Imperial College, focusing on developing computer vision technology for medical imaging applications.

Under Dalyac's leadership, Tractable has become a leader in the field of computer vision for automating visual inspections. The company has grown rapidly since its founding and has expanded its operations to multiple countries worldwide. As of 2021, Tractable has over 200 employees and serves customers in industries such as insurance, construction, and utilities.

Tractable is also led by a team of experienced executives and advisors with backgrounds in technology, finance, and other fields. The company's leadership team includes Chief Technology Officer Adrien Cohen, Chief Commercial Officer Othmane Ktari, and Chief Financial Officer Shervin Khodabandeh.

How Tractable uses Computer Vision?

Tractable uses a range of computer vision tasks to automate visual inspections and analysis in various industries. Some of the tasks that Tractable's technology can perform include:

  1. Object detection: Tractable's computer vision algorithms can identify and locate objects within images, such as vehicles or specific car parts. This is used in the insurance industry to assess the extent of damage to vehicles and in the automotive industry to detect defects in car parts.
  2. Image segmentation: This task involves separating an image into different regions based on similarities in color, texture, or other features. Tractable's technology uses image segmentation to identify different vehicle or construction site components, allowing for more detailed analysis.
  3. Feature extraction: Tractable's algorithms can extract specific features from images, such as the texture or shape of a car part. This allows for more precise defect detection in manufacturing and assembly processes.
  4. Classification: Tractable's technology can classify images based on specific criteria, such as whether a vehicle has been in an accident or a construction site complies with safety regulations.
  5. Optical character recognition (OCR): This task involves recognizing and extracting image text. Tractable uses OCR to read license plates and other identifying information on vehicles.

Overall, Tractable's computer vision technology can perform various tasks to automate visual inspections and analysis, allowing for faster, more accurate, and more efficient assessments across various industries.


In the above article, we had comprehensive information on the importance of vehicle damage detection and the role of computer vision in achieving this task. Using automated systems for vehicle damage detection can lead to significant cost savings, reduce the time taken to process insurance claims, and improve the accuracy of damage assessment.

One notable example in this field is Tractable, which has developed an advanced AI-based platform that utilizes computer vision and machine learning to automate damage assessment. The platform can accurately and quickly assess the severity of vehicle damage, leading to reduced claim processing times and improved customer satisfaction.

Overall, the document highlights the significance of computer vision in the automotive industry and how it can benefit the insurance sector, vehicle manufacturers, and other related industries.

For creating your prototype for a vehicle damage detection system, visit Hands-on With Automated Vehicle Damage Detection. In this article, we have built a small vehicle damage detection prototype using a small dataset.