The authors in this part wanted to share their views as to why InsurTechs are becoming useful entities to re-engineer existing value chains, particularly at a time when insurance firms must invest significant time and effort to keep pace with current change.

You will find these articles cover the full set of insurance activities across the value chain, and other more targeted pieces that observe specific domains of expertise, such as microinsurance, products sold through smart devices or parametric insurance that drives frictionless experiences.

The authors start by reviewing an interpretation of the insurance value chain and offer improvement options and a method to identify those startups that will be the most suited to accelerate internal acceleration programs.

Advanced analytics is a key enabling driver of this change. One author asserts that insurers should go beyond basic demographic segmentation and infer individuals’ psychographics through the use of very specific data sets that are able to improve the accuracy of risk assessments across pre- and post-sales engagement processes.

Micro-policies, or the process of cutting traditional insurance products into smaller, digestible, non-overlapping coverage elements, are at the beginning of their journey, particularly in industrialized markets. Another writer recognizes that reinsurance has changed relatively little in its fundamental form since it emerged from the Industrial Revolution. Still, it is believed that, like many other sectors, it will become more commoditized.

While insurers look for the secret sauce to improve their operations, many of the emerging recommendations point to more intuitive and “as-a-service” infrastructures able to offer and manage smaller bite-size risks or products, volumes of data, and connected systems.

The propagation of connected devices raises an ethical concern from our day-to-day data usage and digital collection, with emerging regulations forcing every organization and government agency to put the customer first, develop new data procedures, and determine these private and public companies’ cyber security exposure and act upon it.

At the core of it all reside data and machine learning algorithms able to provide improved ways of determining and managing risks, moving products from a protection to a prevention status. It is believed that new models will require the creation of new data exchanges able to support large ecosystems of participants, and ease the delivery of transparent interactions.