AI and blockchain technology are having a tremendous impact on the current state of insurance worldwide, not just in the most developed countries but even in countries with fewer resources. Just a decade or fifteen years from now, the landscape might look completely different, and the way insurance services are carried out for individuals may seem very unusual to the people of our days.

AI and its related technologies will have a seismic impact on all aspects of the insurance industry, from distribution to underwriting and pricing to claims. Advanced technologies and data are already influencing distribution and underwriting to some degrees, with policies being priced, purchased, and bound in almost real time.

Purchasing of Insurance

The process of purchasing an insurance will increasingly be faster, with a diminished active involvement from the insurer and the customer. Massive amounts of information will probably be known about individual behaviors, with AI algorithms building up risk profiles, so that the processing time to complete the purchase of an auto, commercial, or life policy will drop to minutes or even seconds. Auto and home carriers might even provide instant quotes for some time but they will eventually refine their abilities to issue policies immediately to a wider range of customers, as telematics and in-home Internet of Things (IoT) devices proliferate and pricing algorithms become more mature and gradually smarter. Some life insurance companies are experimenting with simplified issue policies, but they are mostly reserved to only the healthiest applicants and are usually priced higher than a comparable fully underwritten product. As AI starts permeating almost all aspects of life, subscribers and carriers will be able to identify risk in a much more sophisticated way, we may expect to see a new wave of mass-market of instantly issued insurances.

Smart contracts enabled by blockchain may instantaneously authorize payments from a customer’s financial account in a safer way. Meanwhile, contract processing and payment verification may be completely eliminated or at least streamlined, eventually shrinking down the customer acquisition costs for insurers. The purchase of commercial insurance may as well be expedited as the combination of IoT, and other devices will generate enough data to provide sufficient information for AI-based models to proactively generate the most efficient quote.

Highly dynamic, usage-based insurance (UBI) products will proliferate and will be customized according to the personal behavior of individual consumers. The insurance industry may even shift from a “purchase and annual renewal” model to a continuous cycle, as product offerings may continually adapt to an individual’s behavioral patterns. Furthermore, products could be disaggregated into micro-coverage features (i.e. bicycle insurance, flight delay insurance, diversified coverage for a washer and dryer within the home) that consumers can customize to their particular needs. New products may appear to cover the shifting nature of living environments and travel habits. UBI might become the norm as physical assets will be shared across multiple individuals, with a pay-by-mile or pay-by-ride model for car sharing and pay-by-stay insurance for home-sharing services.

Customized Rates Made “Easy”

Soon, manual underwriting may halt for most personal and small-business products across life and other types of insurance. The overall process of underwriting will eventually be reduced to a few seconds as most parts will be automated and backed by a combination of machine and deep learning models. These models will be run by internal data as well as a huge set of external data accessed through applications and outside data and analytics providers. Information collected from devices provided by insurers and product manufacturers will be aggregated in a variety of data repositories and data streams. These information sources will make taking ex ante decisions possible to the insurers to regarding underwriting and pricing.

Assigning individual tailored policies, which not only makes sense but also doesn’t seem discriminatory, is still a major problem in the insurance industry right now. When this process will be handled by AI algorithms, the impression that certain decisions are being made based on biased factors, will likely go away.

In that sense, regulators may be pushed to review AI-enabled, machine learning–based models, a task that necessitates a transparent method for determining traceability of a score. In order to verify that data usage is proper for marketing and underwriting, regulators may need to assess a combination of model inputs. However, public policy concerns may limit access to certain sensitive and highly personal data (such as health and genetic information) that would lower underwriting and pricing flexibility, and filter out the ones with bad records on relative fields insuring products covered.

Price will always be one of the most important characteristic in consumers’ decision making, but this will push insurance companies to continuously innovate in order to diminish competition on the basis of price. In some segments, the price competition will increase, with slim margins as the norm, while in other ones, individual-tailored insurance offerings will make profits growth and more differentiation happen.

We may reach a point where AI will be dynamically adjusting insurance rates around us on a daily basis, observing and collecting data from our daily lives and submitting them to insurance providers. Thus, pricing may even be accessible in real time based on usage and a dynamic, data-driven assessment of risk, entitling consumers to take decisions about how their actions influence coverage, insurability and, finally pricing.

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