Learnings From Other Industries

The process for gathering enough data so machine learning can detect species in images is the same process for detecting fraud in the vehicle industry.


If current investment figures are any indication, global markets have a great appetite for computer vision, with $21.1 billion of funding received by over 900 companies. And the market for this technology is expected to grow by another 7.8% to $17.4 billion in 2024.

Computer vision makes for a highly compelling case where artificial intelligence (AI) acts as an enabler for the human workforce. It is a branch of AI that helps systems extract actionable information from visuals, such as images and videos. It acts as the "eyes" through which a computer understands and assesses an image. 

In retail, a planogram report that can manually cost around $72 can be done for just $8 using this technology. After working at a data science company, I have learned that the computer vision and visual AI tech used to design supply chain workflows, identify defects or diseases in agricultural produce or research images in pharma drug discovery can all be leveraged to improve the vehicle insurance market. 

Here are ways that AI technology can help insurance companies produce higher accuracy regarding damage detection and claims assessment processes. 

Damage Detection

Already, AI, robotics, IoT and drones have merged with geospatial AI techniques to improve the quality of harvests and prevent crop damage and have redefined the agricultural industry. The connection between controller and data bus ensures that alerts from sensors get communicated to the controller. It generates snapshots and summary reports that go to central information servers and are stored in the cloud. These successes can also be translated to the insurance industry. 

Imagine an insured vehicle has an accident, and the user reports this incident to their insurance company. Through using AI and a computer vision-based model, a drone can be dispatched to the vehicle to get images of the damage from all sides. This accurate estimation can help the insurance provider establish the insurance value even after a car has been used for years. 

These images can be quickly analyzed to check the degree of damage and whether there is a need for repair or replacement. Drones have also been proven to not only bring accurate images but also reduce the risk of any harm to surveyors when the accident site is dangerous to access. 

Detecting Fraud 

Species detection, computer vision, machine learning (ML) and deep learning software use algorithms and statistical models to train computer systems to classify and identify images. I know from working with conservation organizations that you need to have sufficient data of images to train ML models to detect species, which is the same process for detecting fraud in the vehicle industry. 

Video streams from drones can eventually identify damages and understand if the accident is real or staged. The automated assessment of photos and the speedy claim description of the insured party also benefits the insurer: Quick assessments result in less consequential damage and less opportunity for fraud. 

If it is concluded that it was a real accident, the insurance company can look at the level of damage based on conventional data fed into the computer vision-based model. For example, can the bumper be replaced or repaired if it is broken? If there is a scratch, can it be painted or patched up?

See also: 3 Digital Customer Service Strategies for 2022

Claims Estimation

Across life, health and travel insurance industries, AI and ML make it possible to spot anomalies and make claims estimations more accurate. 

Insurance companies can experience better productivity with AI systems as they use automated decision science to quickly analyze accident images, assess damages and identify repair costs in real time. This can accelerate the decision-making and claims process, which used to be done by surveyors in the field. 

AI verifies various intricacies of observing an accident, such as who reported the accident and who was present in the car. This way, computer vision can also check if the consumer has complied with the policy requirements before paying any claim.

The aforementioned scenarios are just the tip of the iceberg of what computer vision can do for the insurance sector. The convergence of computer vision technology with high-quality data analytics tools can provide a competitive edge, especially because global business related to AI in the insurance segment will touch $4.5 billion in 2026. But to see fast innovation and growth, industries must share their learnings, accept there is overlap and cross-reference.

Sundeep Mallu

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Sundeep Mallu

Sundeep Reddy Mallu is the head of analytics and hiring at Gramener, which solves business problems for its clients by identifying data insights and presenting them as data stories.

Mallu advises executives at leading enterprises and NGOs on data science, helping organizations transform by building teams and adopting a culture of data.

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