InsurTech refers to the use of technology innovations designed to squeeze out savings and efficiency from the current insurance industry model. InsurTech exploits big data to formulate deeper insights to human behaviour and personalization of insurance products.
Actuarial science has always been a blend of technical and social sciences. The future increases in both
data volume and variety will require actuaries to have a familiarity with techniques in machine learning,
data mining, image processing and predictive analytics. The ability to effectively apply these learnings
and outcomes will require familiarity with behavioral economics and ethnography.
One of the biggest challenges for actuaries to tackle is the propensity to buy insurance. Traditionally,
tendency-to- buy modeling requires analyzing the purchase behavior and characteristics of people who
purchase insurance. But if you try and think of the flip side of the coin: ‘Why not learn more about the
people who aren’t buying insurance?’ This approach opens up endless possibilities of target marketing.
InsurTech enables traditional insurers to leverage existing data to generate deeper risk insights.
Embracing InsurTech could help insurers gather more insightful and higher quality figures. It would not
only increase the speed of servicing and lower costs, but also open the way for ever greater product
precision and customisation. Behavioural analytics can also help insurance companies gain a deeper
understanding of behavioural trends, customary aspects and habits of individuals, allowing for the
development and creation of customised solutions and better real-time and fast-track customer service.
For example, in-car sensors are already used to measure how safely policyholders drive and offer lower
premiums to more careful road users.
From an external perspective, the key business impact that insurers expect from InsurTech is the
challenge of meeting changing customer needs and the ability to match new offerings with their
expectations. Clients now expect personalised insurance solutions, and “one size” simply does not fit all.
There are many InsurTech solutions focused around consumer messaging. Some examples include
chatbots (virtual advisers), personalized video and natural language generation. One way to maximize
the impact of messages is to leverage behavioral economics. This involves testing hypotheses about the
context in which the messages are presented and how this shapes human decision-making.
Ultimately, the basic purpose and function of insurance is solid, but the delivery and engagement
mechanisms need modernization. It is important for actuaries to keep up with new technologies to
support the increasing variety and volume of data. Companies with a solid understanding of how
people’s daily lives translate into the digital world will have a strong competitive advantage.