6 Use Cases for Computer Vision in Insurance
Insurance is one of the world’s largest sectors, currently worth around $6 trillion, and employing millions of people globally. Over the last few years, machine learning and computer vision in insurance is becoming more commonplace, with more use cases and commercial applications appearing every month.
According to the OECD: “The insurance industry is a major component of the economy by virtue of the amount of premiums it collects, the scale of its investment and, more fundamentally, the essential social and economic role it plays by covering personal and business risks.”
Insurance has been around for millennia, since the first Babylonian Empire. Archeologists discovered stone tablets covering details of maritime insurance policies over 3,800 years ago.
Despite being written on stone in ancient cuneiform script, modern audiences in the insurance sector would recognize the various terms of the policies and the underwriting process.
Since then, insurance, as a commercial means of exchange and risk mitigation strategy, has appeared time and again. Now we all need insurance, in one form or another, whether that’s for our technology, cars, homes, and to insure against ill health and death.
Insurance is a multi-trillion dollar industry, and only recently has computer vision technology started to play such a crucial role in everything from underwriting to claims management.
In this article, we will take a closer look at the various commercial use cases and applications of computer vision technology in the insurance sector.
How is Computer Vision Technology Being Deployed in the Insurance Industry?
An article in Life Insurance International says that one of the reasons AI technology is so popular in the sector is that it “enables accurate underwriting and a smoother claims process, which ultimately improve the customer experience across the policy lifecycle.”
Computer vision applications in the insurance sector improve the customer experience. However, and more of a mission-critical use case for insurance companies, CV models help them to reduce costs, risks, and improve the analysis of claims, processing, and underwriting.
In simple terms, CV and other algorithmically-generated models save the insurance industry money, and generate enormous annual value. McKinsey estimates that the use of traditional and advanced AI models, such as computer vision could increase revenues and generate cost savings that total over $1.1 trillion across the insurance sector.
Whether that’s reducing human processing time and manual labor, improving underwriting efficiency so that insurance companies can charge the right amount for premiums, or reducing the amount being paid out, saving money is crucial in a sector that thrives on creating shareholder and stakeholder value.
Here are 6 use cases and commercial applications for computer vision in the insurance sector, and we will cover each in more detail next:
- Improving Claims Processing for Car and Home Insurance
- Faster and Safer Insurance Claims Adjudication
- Digital Documentation Upload Using Optical Character Recognition (OCR)
- Facial Recognition, Emotion AI and Computer Vision for Fraud Detection in Financial Services
- Industrial Insurance Policies: Forward-projecting Risk Assessment and Underwriting
- Artificial Intelligence in Underwriting Automation
6 Use Cases and Commercial Applications for Computer Vision in the Insurance Industry
Improving Claims Processing Speeds for Car and Home Insurance
Computer vision technology improves the speed and accuracy of insurance claims, and is a commercial application being implemented by a number of insurance tech startups. Both consumers and insurance companies want claims handled quickly. And yet, it’s a difficult balance between speed and accuracy, especially when there's the risk of fraudulent claims slipping through the net.
Computer vision technology solves both problems at the same time, and CV solutions achieve this faster and more efficiently than traditional methods. Dozens of market-leading multi-billion dollar insurance companies are using this, and so far one computer vision startup is responsible for helping 1 million households, resulting in $2 billion in claims processed more efficiently.
Trained computer vision models are used to assess the nature of home or car-related damages (using pictures or videos), appraise the value of the damage, and evaluate whether further investigation is needed, especially when there's a risk the damage could be faked in order to claim from an insurance company.
Faster and Safer Insurance Claims Adjudication
Would it surprise you to know that property damage insurance claims adjusters are 4x more likely to suffer an injury on the job than construction workers in the US? Assessing insurance claims can be dangerous, and work-related injuries cost insurance companies time and money.
Instead of sending in human claims adjusters, drones equipped with computer vision technology can more easily and safely assess any damage, submitting those visual reports for analysis. Especially when damage is in a difficult-to-reach location, such as higher floors on buildings, roofs, and industrial sites.
Computer vision models used by insurance firms can then assess asset damage after natural disasters more effectively and safely than humans using this drone footage. In some cases, this would eliminate the need for in-person inspections altogether, saving insurance companies money during the claims adjustment and pay-out process.
Digital Documentation Upload Using Optical Character Recognition (OCR)
Most traditional insurance companies still rely on hand-written forms. This produces hours of extra work when handling insurance claims, onboarding new customers, and the dozens of other manual tasks that could be greatly reduced with the use of document digitization.
When insurance firms combine Optical Character Recognition (OCR) and computer vision models it makes a huge difference to operational efficiencies. OCR tools read text and numbers, turning paper documents into digital versions.
Combine that with CV models and insurance firms can make a massive 80% saving on countless legacy processes, such as Know Your Customer (KYC), and claims triaging, according to McKinsey research.
Facial Recognition, Emotion AI and Computer Vision to Reduce Fraud in Financial Services
Did you know that when humans lie there are up to 54 different micro-expressions we make? Tiny changes in facial expressions are subtle and subconscious giveaways for when a person is lying, or trying to deceive someone.
Technology already plays a massive role in fraud prevention. And it’s in the insurance sector where fraud is a serious challenge.
In the US alone, insurance fraud costs the sector over $308 billion. At least 85% of insurance organizations have a dedicated fraud team, trying to prevent fraud and recoup billions in fraudulent payouts. In 20% of cases, some form of fraud was suspected in insurance claims, according to recent statistics.
Companies in this sector will take any reasonable measure to prevent and reduce the risk of fraud. One tech startup, Ping An, is providing CV-based and AI-powered software to the insurance industry to help them detect those micro-expressions that indicate someone is lying.
The insurance and financial giant, UBS, is also developing in-house software that runs on the same principles, in an attempt to reduce fraud.
Industrial Insurance Policies: Forward-projecting Risk Estimation and Underwriting
Industrial machines are expensive to insure. If something goes wrong, they’re even more expensive to fix and can cost a manufacturer millions in lost revenue.
Fortunately, the technology now exists to plan ahead more accurately when a machine needs preventative and proactive maintenance or parts replacing. Ensuring manufacturers avoid costly downtime, and the insurance business reduces its need to make payouts. This technology is known as the Artificial Intelligence of Things (AIoT).
In other words, using real-time visual sensor data (from IoT devices), combined with machine learning algorithms, can accurately detect when a manufacturing machine is at risk of failure. It takes the risk out of risk management. With an AIoT and computer vision-based solution, manufacturers and insurance providers can forward-project risk estimation and take steps to mitigate those risks, aligning repair cycles with when repairs are actually needed.
Artificial Intelligence in Underwriting Automation
Although there are many more commercial use cases in the insurance industry for computer vision models, the final one on our list is the application of artificial intelligence in underwriting automation.
Underwriting is a Babel-style tower of different software applications. Underwriters spend a considerable amount of time transferring data from one app to another, checking it, and ensuring everything is correct. Otherwise known as dataset “janitor work”, as The New York Times once eloquently put it.
Unclean, duplicate, and poor-quality data costs the global economy $3.1 trillion, according to an IBM estimate published in the Harvard Business Review (HBR).
Data cleaning for computer vision projects is an expensive and difficult task, albeit one that’s crucial to the final outcome of these projects.
Insurance companies need a solution to this problem. The use of Natural Language Processing (NLP) is one such solution, especially since 80% of the data insurance providers handle is text-based. However, AI-powered optical character recognition (OCR) and computer vision models are also proving valuable when it comes to reducing manual tasks and increasing automation for underwriting teams.
As we have seen, there are already numerous applications for computer vision technology in the insurance sector. Computer vision algorithms are generating billions in savings, from fraud prevention to making self-driving cars safer, and claims processing faster and more efficient.
Let’s see what new computer vision innovations 2023 brings to the insurance sector!