While the insurance industry has struggled with adopting new digital paths, insurance companies have noticed the growth of highly digitized new market entrants, some of which they may want to emulate. However, as we have seen from past experiences, the copycat approach is often the least successful as only tested breakthrough ideas can yield high financial returns. In just 6 years and despite the pandemic, InsurTech startups are now counting at least 23 well-known unicorn startups within their ranks or insurance companies with a billion-dollar valuation. These digital insurers and service providers use digital technologies to design, deliver, and price insurance offers uniquely. We estimate that the total valuation of our selected 23 companies today represents over $60 billion in valuation within an 850 strong global portfolio worth $2.4 Trillion.

It is no surprise that the insurance sector at large has been guilty of negligence at one particular front, industry Innovation. Although, Insurtech startups have come thick and fast in the nick of time, yet their broad impact on the industry is marginal at best and meager otherwise. Given the era of uncertainty that we are scraping through, the rate of innovation in insurance segments needs to pick up if the underwriters of today are to thrive tomorrow. In this article, we'll be focusing on the possible measures they can implement to do so. Insurance is not the ball game it once used to be when the incoming customer used to feel indebted to a company for assured support in challenging times.

A Bellevue-based insurance startup that uses artificial intelligence to sell personalized insurance options has been sold for $2.35 billion to industry giant Prudential Financial. Assurance IQ, an online insurance-tech company that started in 2016, meets its customers through an online portal instead of through a financial adviser. It sells life insurance, auto insurance, health insurance and Medicare options online from over 20 providers. The company says it has sold at least one insurance product to 300,000 customers and the site has had more than 18 million visitors." To the 144-year-old insurance titan, the 3-year-old startup's value lies in its online-first platform, efficient data collection and artificial intelligence to find personalized insurance at a much faster rate, executives said.

Enhance operational efficiency, improve time-to-market, enable a more intelligent way to sell and service, and more. During the last five years, industrial use of AI – in terms of interest, investment, ideation and implementation – has risen exponentially. The proof of the pudding is in the eating. This can be validated through how a leading health insurance startup leveraged AI by using data and software to build clinical profiles of people to identify gaps in care. These gaps in care were filled with visits and free choice of doctors for patients to avoid costly hospital stays. Similarly, a life insurance startup used AI to generate quotes for accident death claims which simplified sign-up in less than 2 minutes. There are several such instances that fortify AI as a technology that would revolutionise businesses and the world at large.

In spite of those problems, in the last decade, we noticed a new trend emerging. Insurances, in the effort of trying to reduce moral hazard problems, they started offering premium discounts to their final customers in order to get extra information. This occurred either through a questionnaire (asking directly the customer for further data in exchange for a lower price) or indirectly through devices (healthy devices, black boxes, etc.). The real issue though has been the engagement side of this proposal, because of the opposite nature of information, rewards, and human nature. The rewards offered were indeed either temporary or provided only once and people got lazy very quickly, while the information stream needed to be constant.

The insurance sector is one of the most old-fashioned and resistant to change -- so artificial intelligence will have an even greater effect. The insurance sector is one of the most old-fashioned and resistant to change, and this is why AI will have a greater impact on that with respect to more receptive industries. The collection of data of new types (i.e., unstructured data such as reports, images, contracts, etc.) and the use of new algorithms are disrupting the sector in several ways. This is a really simplistic representation of the insurance business in the last fifty years, and I am aware that insurance experts might disagree with me in many different ways. There are a couple of further features to be pointed out: first of all, insurance has historically been sold not bought, which means that brokers and agents were essential to tracking new customers and to even retain old ones. In addition, it is an industry which is by definition rich of data because they collected anything they could, but is also one of the less advanced because either many of those data are unstructured or semi-structured, or the model used are quite old and simple.

The insurance sector is one of the most old-fashioned and resistant to change -- so artificial intelligence will have an even greater effect. The insurance sector is one of the most old-fashioned and resistant-to-change space, and this is why AI will have a greater impact on that with respect to more receptive industries. The collection of data of new types (i.e., unstructured data such as reports, images, contracts, etc.) and the use of new algorithms are disrupting the sector in several ways. This is a really simplistic representation of the insurance business in the last fifty years, and I am aware that insurance experts might disagree with me in many different ways. There are a couple of further features to be pointed out: first of all, insurance has historically been sold not bought, which means that brokers and agents were essential to tracking new customers and to even retain old ones. In addition, it is an industry which is by definition rich of data because they collected anything they could, but is also one of the less advanced because either many of those data are unstructured or semi-structured, or the model used are quite old and simple.