It’s been almost 70 years since the invention of artificial intelligence, or AI. Several decades ago, when the technology was still new and seemingly fresh out of a science fiction novel, most people assumed that it would be used by a few niche industries. But outside of obvious applications like computer engineering, transport, and security, artificial intelligence and the related field of machine learning (ML) have since been adopted into the mainstream. One example of an industry that’s recently welcomed the use of AI and ML is the insurance industry.
To those who aren’t very familiar with AI or ML, or whose understanding of the insurance business is limited to traditional insurance experiences, this might seem like a surprise. But truth be told, there’s a lot of sense in utilizing more AI and ML technologies for modern insurance delivery, for example through the use of an insurance management system that’s capable of machine learning. If you’re part of the insurance industry, it will be a highly practical decision on your part to explore these technologies. To illustrate why, here’s a briefer on the impact that AI and ML have made on the insurance business.
What Do AI and ML Have to Do with the Insurance Industry?
First, let’s define the key terms in question. Artificial intelligence, or AI, is the term for any technological apparatus that can mimic human behavior. Machine learning, or ML, is a subset of AI in which a machine can automatically learn from past data without being explicitly programmed by a human to do so.
The more data the machine observes, the better it becomes at recognizing meaningful patterns—and therefore, the better it becomes at “learning” in the human sense. This concept is important when we make the connection between ML and the realities of the insurance experience.
Those who have worked in the business for a long time know that insurance relies heavily on data. The company deals with large, complex swathes of data pertaining to customers’ health, financial data involved in underwriting and product pricing, and other data involved in day-to-day operations. But most legacy insurance systems only utilize a fraction of that data, and even when they do, the task of culling and processing that data is extremely taxing on them.
AI and ML technologies, however, have changed the game. Machine learning, in particular, has the potential to make an insurance company more data-driven than it already is. The results of such a data-driven approach are quite favorable to the insurance business. Thanks to machine learning consulting services that unlock a 360-degree view of all the data in the insurance process, insurers can be more agile and responsive in their decision-making.
4 Insurance-Related Tasks That Will Benefit from Machine Learning
There are four areas of the insurance experience that will benefit from extensive use of machine learning. They are the following:
First, ML can assist underwriters in the already difficult task of writing policies. The technology will allow them to digest large and complex sets of data that pertain to risks and potential losses, and therefore arrive at the most competitive prices.
2. Claims Processing
ML can also help insurers improve in the area of claims management. First, it makes an instant and up-to-date breakdown of claims costs possible for insurers. Second, it allows them to kickstart a more targeted claims processing approach. If ML technology is used wisely, insurers can save a lot of money through sheer efficiency at claims processing. Best of all, insurers can preserve their customers’ trust in them.
3. Insurance Fraud Detection
On the flipside, ML can also guide insurers to detect the erratic patterns that often signal fraudulent insurance claims. Insurance companies will be able to amass their skills at handling unstructured, semi-structured, and structured data sets to predict incidences of fraud and deal with any threats before it’s too late.
4. Customer Experience
Lastly, ML algorithms can survey the data customers put on their profiles when they’re applying for insurance or renewing their existing policies. That data can be the basis of computer-generated insurance advice, which will be both accurate and personalized according to the individual customer’s needs. This opens the opportunity for insurers to improve their customer service, as well as adopt more effective strategies for upselling other insurance products in the customer’s preferred channel.
Artificial intelligence and machine learning technologies can definitely help insurers take care of the data-related aspects of their business. But they won’t be a replacement for the human element of insurance work. Frankly, they shouldn’t need to be. Rather, AI and ML can be thought of as complementary tools to be used alongside others in the insurer’s toolkit, like marketing and strategic planning.
It’s up to insurers to combine modern technologies with warmth, hospitality, and other timeless human values that define their business. Doing so will definitely bridge that gap between a company’s past successes and future growth opportunities, especially in the digital era.
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