Top Applications of Computer Vision in Insurance (2022 Guide)
Top Applications of Computer Vision in Insurance (2022 Guide)
Computer vision will make significant changes in the future value chain of insurance, and the potential and use cases are endless. Risk assessment systems powered by AI work better when they use risk assessment, automation that AI sets off, and predictive analytics.
Traditional insurance models, like risk pooling and risk individualization, could be turned on their heads by new risk management technologies. This could cause a lot of trouble.
In the insurance industry, some of the most important uses of computer vision and artificial intelligence are risk management for policies that are already in place, risk estimation for new policies, claims management, and real-time monitoring of assets or processes.
This guide will briefly discuss computer vision applications and show how computer vision will transform the insurance industry in 2022.
Computer Vision Applications
Machines are given the ability to "see" and navigate their environment with the help of computer vision, which is powered by machine learning and deep learning algorithms.
Because it can be used on a large scale, this powerful technology is now an important part of technological progress and digital transformation in many different areas.
But, how can computer vision help businesses? Let's look at some computer vision applications briefly:
CV in Retail Management
Retail stores are already using computer vision solutions to track shoppers' movements. This makes loss prevention less intrusive and more customer-friendly. Computer vision is also used to determine how customers feel and make ads more relevant to them.
Vision solutions powered by AI are also used for programs to keep customers, keep track of inventory, and evaluate product placement strategies to increase ROI.
You can check out this article about what you can gain by using computer vision in the retail industry.
CV in Transportation
Computer vision is at the heart of the transportation industry's rapid technological progress, driven by the industry's growing needs.
The Intelligent Transportation System (ITS) has become a key area for making transportation more useful, effective, and safe. This includes everything from self-driving cars to sensors that tell when a parking spot is full. Computer vision recognizes and detects objects (like road signs and traffic signals), makes 3D maps, and estimates motion, and it is a big part of how autonomous cars are being made. If you are looking for a no-code platform to detect objects, try Cameralyze Object Detection now.
Detecting and following people has become an important area of computer vision research because it could affect how pedestrian protection systems and smart cities are designed.
CV in Manufacturing
Computer vision is widely employed in AI-powered inspection systems in industrial facilities. Such systems are common in research and development labs and warehouses, and they help these places work smarter and better.
For example, computer vision is used in the inspection systems of systems that do preventive maintenance. By looking at the environment all the time, these tools cut down on the number of machine breakdowns and product flaws. If the system finds a likely breakdown or a low-quality product, it alerts people so they can do something about it. Aside from this, workers use computer vision to package products and control their quality.
For more detailed information on why the use of AI in Manufacturing is important, check out this article.
CV in Agriculture
Most people do not think of agriculture when they think of cutting-edge technology. But old methods and tools are slowly being phased out of farms worldwide. Today, farmers use computer vision to make their farms more productive.
Companies that work on agriculture technology are making more advanced sowing and harvesting models using computer vision. These solutions can also be used to get rid of weeds, find out how healthy plants are, and do more advanced weather analysis.
Computer vision has many current and future uses in agriculture, such as crop monitoring by drone, automatic pesticide spraying, tracking crop yields, and smart crop sorting and classification. These AI-powered solutions look at the crops' shape, color, and texture to find out more about them. With the help of computer vision technology, more and more things are being done with weather records, forestry data, and field security.
As you can see, computer vision can be used effectively in many fields that use image and video data. And here's the good news: Cameralyze offers a no-code computer vision platform for any use cases (and more!)
To learn more, just head over to:
Computer Vision in Insurance
Let's see how computer vision can transform the insurance industry.
Preventing And Identifying Fraud With Computer Vision
Fraudulent insurance claims have affected the entire sector. One problem could be that delaying reimbursements or doing long-term investigations during a hard time could make customers unhappy. In addition, insurance industry regulatory pressures and the expense of conducting investigations. However, wrong payments cut profits and encouraged other policyholders to act dishonestly.
Insurance fraud is hard to catch because there are so many different ways to do it, and only a few real cases are found in representative samples. When designing fraud detection algorithms, it is important to weigh the potential for false alarms against the potential for savings. With the help of machine learning and computer vision, it can be possible to make fraud prediction algorithms using past data.
For example, a computer vision model could be taught to spot and flag strange things, such as fake documents or photos. Natural language processing (NLP) tools can look at unstructured data like communications, claims, and consumer feedback to detect possible fraud and warn people. Because of improvements in computer vision technology that make predictions more accurate, loss control units can now cover more ground with fewer false positives.
A common use of computer vision is to use behavioral data like facial expressions or the tone of voice during underwriting. For example, behavior monitoring alone is expected to give more than 40% of the risk information in life insurance or health insurance.
You can also read our article to learn more about facial expression recognition: The Future of Emotion Recognition in Machine Learning and AI.
Vehicle Damage Assessment With Computer Vision
When an accident causes a car to crash, the person at fault gets insurance and has a collision damage assessment done. The main goal is to determine what repairs need to be done and how much money will be returned. This is a manual job that takes a lot of time and people. The assessor needs to carefully and accurately estimate all the damage to determine which parts of the vehicle need to be fixed or replaced.
With computer vision, it only takes a few seconds to find the damage and figure out how bad it is. This makes the work of assessors easier and helps them figure out the right amount to claim to solve the problem. If you are thinking about making such an AI model, keep in mind that data quality is key to making it work.
Roof Underwriting with Computer Vision
From the point of underwriting, a roof is one of the most important parts of a property. Insurance companies have always had difficulty figuring out how old a roof is. In the past, insurance companies relied on what the homeowner or agent told them, what an inspector saw, or the year the house was built.
Standard procedures can get the roof's age wrong, costing insurers an estimated $1.14 billion a year in lost premiums. The underwriting workflows are also made more complicated by things like property changes and bad weather like high winds and storms.
Insurance companies can use computer vision to find out a roof's age, condition, and other details, as well as how likely it is to be damaged by hail or wind. This helps insurers in two ways: it speeds up the underwriting process and makes it easier for them to predict future risks. With high-resolution views and aerial photos, you can see how the roof changes over time and spot potential problems, like trees that are close by or hang over it, materials that are easy to damage, and expensive additions, like solar panels.
Monitoring of Building Sites with Computer Vision
Homeowners who have just built or bought a new house should protect their investment by getting homeowners' or another type of property insurance. Another option is builder's risk insurance, which protects a property owner from financial loss while building their structure. Most of the time, this kind of insurance will protect the building and the materials used to build it. The construction firm also provides insurance for its workers.
To keep construction sites as risk-free as possible for workers, several companies are using computer vision monitoring systems. Particulars on the following are necessary for the latter's application:
- Is the appropriate technology being used by qualified personnel?
- If there are plans for accidental damage if procedures go as planned if there are signs of an impending failure that would be covered by insurance, and so on.
In any case, a surveillance system that uses computer vision technology can monitor any of these situations in real time.
CV In Insurance To Automate The Underwriting Process
Several different software applications are often used to describe the value chain of office processes. Underwriters spend a lot of time manually moving data from one software program to another. They do not have much time to do more valuable tasks like making decisions based on information, selling, or talking with brokers.
The text makes up about 80% of the data in insurance companies today. So, natural language processing (NLP) is one of the most widely used AI technologies. Here, AI automates interactions, creates cognitive applications, and uses semi-structured data to automatically provide relevant information. Underwriting tasks involve a lot of documents, most of which are written on paper. For this reason, getting structured information from scanned documents is important.
AI-powered optical character recognition (OCR) saves time and reduces the amount of work that must be done by hand. The data taken out can be used to make suggestions for the underwriter by comparing it to similar cases.
Bottom Line
A great deal of complexity is involved in developing computer vision-based artificial intelligence insurance applications for large-scale systems.
For this reason, we created Cameralyze, a platform for creating computer vision applications without the need for programming. For insurance industry giants, it's much simpler and quicker to roll out enterprise-level, secure, scalable computer vision solutions.
The complete application lifecycle can be covered by the end-to-end solution when it comes to deep learning vision systems. Without starting from scratch and writing code that is both difficult to maintain and expensive to update, the unique no-code and low-code capabilities provide a 10x faster time-to-business value.
Click here to start using Cameralyze!
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