Insurtech Firms : Disruptive Innovation or Next Gen Insurance?
posted about 2 years ago
posted about 2 years ago
Author: Adrian Kinnersley | CEO
Introduction to InsurTech
Like its neighbour 'fintech' in the finance industry, 'insurtech' is making inroads into traditional insurance practice, with technological innovation breaking the boundaries of centuries old procedures. Big brand leaders such as CIGNA, Farmers and Allstate may continue to rely on their actuarial tables and risk categories, but new insurance technologies can offer increased efficiency and cost savings which may eventually leave traditional techniques in the dust. Older and more established companies have their reputation and long-standing financial stability to cover big risks, but insurtech firms are working on other areas in the business which have less appeal to the lions. These include dynamic data acquisition, where IoT (the Internet of Things) can provide constantly updated data streams on which customized policies can be based.
Changing Consumer Demand
One of the problems with the traditional system is that actuarial tables are based on averages, so that some people are shoe-horned into categories that don't quite fit them (and for which they often pay over the odds). The insurer will usually only assign categories where there are enough examples on which to calculate an average, and for the company to derive a profit. With insurtech firms, this broad degree of categorization is not acceptable. Assessing the impact of insurtech in 2016, PwC predicted the emergence of more UBI (usage-based insurance) models as a response to customer demand for more self-directed solutions.
Advanced Data Management
Insurtech is capable of increasing the data stream so that there is more to analyze, including any area where data is produced and tracked. This can be anything from social media information to wearable tech, such as step counters and cardio monitors, GPS trackers and commercial activity analysis. Artificially intelligent computer programs then use deep learning techniques to develop complex data sets based on user experience to increase transaction efficiency. An advanced knowledge of individuals and trends can be built out from these data sets, so that AI programs can more precisely match policies to people and to individual circumstances, including instant, on-demand insurance for one-off or short-term events.
Prevention over Protection
With far more peer-to-peer activity going on via distributed apps and blockchain management platforms, data communications are now widespread and immediate. Collection and analysis of data on such a scale, and in such a minute degree of detail, enables a shift in insurance models from protection, or reactive solutions, to prevention, or proactive solutions, based on a granular analysis of circumstances. Transparency is also a key factor, and can result in better priced policies for more people, including specialized group coverage and discounted rates. At the other end of the scale are personalized policies for people who have only sporadic, localized or occasional risk.
Many traditional roles and models are being disrupted by technological innovation, and cautious old school practitioners are under threat from a new generation of insurtech firms exploiting the quantum shift in industry practice. In today's climate of ever-faster development, the answer to such a threat is not to resist the tidal wave of change but to ride it, keeping up with the momentum of innovation, working with insurtech firms wherever possible, and recognizing that today's world is one of constant and generally beneficial change.
As with so many 21st century industry developments, working with insurtech requires new skills, so the smart insurer should be looking now to hire dynamic and tech-savvy talent who can put the benefits of insurance technologies to good use.