Adopting Computer Vision in Insurance: Vehicle Damage Assessment Case
Digital transformation is inevitably approaching businesses within the insurance industry, raising new challenges and bringing opportunities simultaneously. The use of modern technologies can improve the way insurers interact with their customers, streamline operations, enhance risk assessment and management, and a lot more.
Back in 2021, the global market size of AI hardware, software, and services in the insurance industry accounted for $3.1 billion. The compound annual growth rate (CAGR) of 32.6% for this niche is predicted for the 2022-2030 forecast period. The tendency seems to be solid. And, since the growth tempo is high, the market is still far from being saturated.
As of 2023, the usage of computer vision in AI for insurance purposes is rapidly increasing. Probably, one of the frequent implementations of the technology is the automated inspection of cars during insurance claim processing.
Let’s find out how computer vision technology transforms the insurance sector and what business benefits it brings, based on the example of visual inspection of vehicles use case.
Computer vision in insurance: car damage assessment
Consideration of insurance claims is often closely connected to a large load of manual document processing and property damage assessment. No wonder insurance companies may experience an increased load of vehicle damage claims, especially during peak seasons. That’s exactly when AI-driven car damage inspection comes into assistance.
To conduct damage detection, computer vision algorithms process post-accident vehicle imagery. Smart systems can recognize a car validating whether it’s the same one as the documents evidence, identify damaged car elements, and estimate preliminary damage and repair costs.
While the conventional insurance claim processing workflow is complicated and time-consuming, computer vision-enabled one is narrowed down to only four following steps:
Building a well-performing system for car damage detection with computer vision may be complex. Don’t hesitate to reach out to our team of well-seasoned AI and ML engineers should you need assistance.
Computer vision for car damage detection: what types of damage can be detected
During car inspection with AI, algorithms use digital pictures of a vehicle being examined to detect the following types of damage:
Deteriorations of the bumper, hood, doors, dickey and other metal car parts are classified as forms of metal damage:
Dents and deformations. Concavity on the metal surface caused by pressing the metal body inside is known as a dent. It’s often caused during car crashes when there is destructive pressure from external objects.
Scratch. When a hard or sharp object is moved against a metallic surface, it causes scratches. It’s the most prevalent type of car damage, and it may largely vary in its severity.
Tear. If a vehicle sustains extreme forces, the metal parts may be split into pieces. This damage is called a tear. It often affects the external surface of a car part as well as results in additional destruction to the inside of the car.
Metal damages are quite easy to detect, as they are well-visible. But the tricky part is the assessment of the destruction’s magnitude. Yet, using deep learning techniques in computer vision allows the system to solve even such complex tasks.
Deteriorations of the windshield, back glass, car windows, headlight, and taillight are classified as forms of glass damage:
Crack. This surface damage may be caused by physical forces, extreme temperatures, weather conditions, extreme pressure, or combinations of these factors. It typically looks like a net of circles and lines that indicate surface areas where the glass integrity was compromised.
Chip. When a small chunk of glass comes out of the surface, it’s called a chip. Professionals refer to it as a “stone break” or “pit.” This damage doesn’t penetrate the glass of the way, and consequently, it doesn’t cause longer cracks.
Spider crack. If there was a heavy physical impact caused to glass by an object, like a rock, a spider crack is formed. It often looks like a small hole in the center, where the force was applied — an impact point. Multiple cracks are formed around this area, giving the glass surface the appearance of a spider web.
Usually, the severity of glass destruction can be both spotted and assessed quite accurately.
Damages that are not categorized as either metal or glass damage or are not forms of impact damage fall within this definition. Examples include dislocation or replacement of car parts, gaps between car parts, missing parts, cosmetic damage done, for example, to car paint, and more. The capabilities of computer vision algorithms to spot non-destructive damage are limited, but they still may have something to offer.
Computer vision-assisted vs manual car damage assessment
While manual vehicle inspection is prevalent, computer vision-assisted one gradually becomes complementary to it. In some cases, it even completely replaces assessment by a human worker. Let’s have a look at the most significant pros and cons of both types of car damage detection.
Main pros and cons of computer vision-powered car damage detection:
Vehicle damage assessment with AI has the following strengths to offer:
Workforce utilization and safety. With AI-driven technologies, the involvement of labor in the vehicle inspection cycle can be minimized. Not to mention that the entire claim processing can be done remotely. It results in better worker safety as inspectors are not vulnerable to electrical, moving parts, slip and fall, and chemical exposure hazards.
Cost savings. Automating simple insurance claim processing can significantly lower operating costs. After all, everyday cases like scratches or glass cracks are easy to handle for the AI system, yet time-consuming for human inspectors. Implementing a computer vision-based vehicle inspection system can lead to considerable savings for businesses.
High efficiency. Typically, it takes a computer vision system only a few seconds to process a single image. It makes working with batches of images incredibly time-efficient, allowing for more inspections to be completed in less time.
Here are two limitations of computer vision systems for car inspection:
Accuracy depends on the image quality. Low image sharpness or inappropriate lighting may largely deteriorate image processing. Besides, there are still risks of vehicle owners intentionally making fake images or taking photos in the way it alters the view of the severity of the damage.
Some types of damage cannot be detected. In the case of deteriorations that don’t categorize as glass or metal ones or are hidden damages, image processing may not be enough for an accurate estimation. AI-assisted vehicle inspection is also at a higher risk of fraud, but engineers may mitigate the issue by applying additional fraud-detection algorithms.
AI-driven damage detection is of great usability. Yet, it’s important to understand that the scope of its usage has restrictions.
Main pros and cons of manual car damage detection
The strengths manual vehicle damage assessment is appreciated for are the following
High reliability of damage assessment in specific cases. Trained inspectors can quite accurately estimate the severity of deterioration and the potential repair costs. They also can spot hidden damages, making human-powered assessment better for cases of inspection of flooded or extensively used vehicles.
Widely used approach. Since manual vehicle inspection is recognized as an industry-standard approach, finding specialists and establishing the optimal workflow should not present a difficulty.
Here are two limitations of the conventional approach to car inspection:
High rate of labor resource usage. In the conventional inspection cycle, manual labor is involved in nearly every step. It increases the probability of human error as well as brings difficulties related to human resource management.
Time-extensive procedures. Both document approval and manual car damage inspection take considerable time. This way, a single claim completion may take days and even weeks.
Let’s summarize the key points of both car damage detection techniques and compare them against each other.
It’s difficult to claim that the CV-assisted method of car damage detection will completely replace the manual one in a short while. Yet, its capabilities are totally enough to take on large batches of assessment cases. It’s also interesting to know how else machine learning optimizes insurance claims processing.
Business benefits of car damage detection with computer vision technology
Let’s review some business benefits of incorporating computer vision for car damage assessment:
Higher customer satisfaction. One of the frequent client complaints is that their insurer considers insurance claims for far too long. AI systems can partially relieve inspectors from handling loads of claims. This would instantly increase service quality and, consequently, customer satisfaction.
Optimized expenses. Setting up a computer vision system for vehicle assessment may seem to be a costly investment. Yet, it’s an incredibly cost-effective intervention in the long run, as processing minor cases with computer vision takes fewer resources compared to manual inspection.
Lower rate of manual labor involvement. Labor-extensive processes may be challenging to set up and maintain in the optimal state. Hiring and training staff, human error, and labor safety are only a few of the factors that should be considered carefully.
Enhanced digital transformation. Computer vision-enabled car inspection is one of the ways in which companies in the insurance sector may get a technological advantage over competitors. So, this option should not be overlooked.
Improved brand image. Technological advancements and a wide choice of options are what many appreciate. Mere knowing that the insurer offers such an option as online vehicle damage claim processing already makes clients trust their provider more.
It’s easy to notice that the usage of computer vision technology in insurance promises plenty of business opportunities.
Computer vision applications in insurance industry
Let's review other most common computer vision tasks of AI-driven systems within the niche:
Insurance claim fraud happens in the industry quite often. Naturally, companies aim to minimize efforts on case investigation and lower the rate of incorrect payouts, as it has a direct impact on the brand image and business profitability. In this computer vision use case, the technology assists in identifying invalid documents or detecting fake images. Some other forms of analyzing unstructured data may also be applied.
Roof condition assessment
Checking the roof's age, condition, and characteristics, as well as estimating its potential vulnerability to external forces, is both time-consuming and physically challenging for inspectors. Insurance companies often have to rely on information provided by homeowners, which results in additional business risks. With computer vision for property risk assessment and high-resolution aerial imagery, inspectors can get estimations of the roof conditions of thousands of buildings with significantly fewer efforts.
Wildfire risk assessment
In wildland-urban regions, wildfires are a frequent cause of property destruction and insurance losses. Calculation of the hazard score most often relies upon aerial imagery processing. Computer vision helps to measure elevation, acreage, and defensible space between structures and vegetation and even detect the presence of potentially combustible materials. This way, insurance companies may get a precise assessment of risks related to providing insurance services to owners of a particular property.
Surveillance on construction sites
The construction process is associated with large labor safety and on-site construction material damage risks. Computer vision-powered surveillance system is often implemented to minimize unfortunate occurrences. It assists in monitoring the usage of necessary equipment, execution of processes, signs of potential failures, etc., in real time.
Before going deeper into the topic, it may be quite useful to learn more about computer vision applications and capabilities. Computer vision projects in insurance are numerous, and so are ways in which they can give a business a competitive advantage.
AI systems find their usage in insurance in a variety of ways, from vehicle inspection and wildfire risk assessment to fraud detection. Processing of car damage insurance claims is resource-extensive, so some automation comes here in handy, It’s still hard to claim the complete superiority of computer vision-enabled car inspection over a manual one. Yet, if such a smart workflow is established and utilized wisely, it may complement conventional procedures bringing tons of business benefits.
Implementation of computer vision in insurance requires high expertise. If you aim at getting the most out of such an investment, then let a team of AI and ML professionals from Interlliarts contribute.