The insurance business largely depends on the availability of data. Mathematical models that define insurance premiums require information (at least) on claims frequency and severity. Insurance companies have been using this type of data for centuries. Moreover, the rise of digitalisation unlocked new data sources. The ideal mix of both old and new data sources enables insurers to provide individualised risk pricing and hence, optimize distribution strategies.
Often policyholders are treated as one homogeneous risk segment (or 6 for a private liability in Germany) and accordingly receive the same/similar quote despite significant differences in their actual risk profile. For few insurers, this might be the status quo, yet the leading insurers are transforming their underwriting engines to achieve excellence that can lead to:
Boost competitiveness.
Insurers, particularly incumbents, possess a wealth of historical data – often unavailable as millions of non-digitalized/non-structured documents. New technologies in the data analytics field enable them to gain a competitive edge over big tech companies that have begun to disrupt the insurance industry. One of the recent examples that made headlines is Tesla Insurance - utilizing its consumer proximity and behavioural data to change the way automobile insurance premiums are calculated.
Fairness and strengthening the ESG position. In an ideal world, every insured would have personalized pricing composed of countless variables and flawless data. The granularity of premium calculation provides fairness and covers vulnerable parts of the population for which insurance might have been considered a "luxury". As a company committed to the highest ESG standards, we strive to reach this ideal.
Superior insurance product
New economic and market reality requires insurers to enhance product development strategies. At the same time, increased consumer expectations drag the focus from the product to the customer. Insurers are demanded to increase their product development agility, improve speed to market and continuously refine the customer experience. Investing in data capabilities is one of the ways which helps to manoeuvre between these goals.
“Data as a Service” in the B2B World
We at ELEMENT invest in internal business intelligence competence as well as streamline data-driven product creation. It serves as a gateway to support our partners with a superior insurance product and provides additional value. As we treat each partner with dedication and strive to deliver as much customization as possible, we align together on available data sources for precise underwriting. Our support extends beyond the Go-Live date: we offer data and analytics capabilities to our partners where needed. We can draw insights that help them to improve distribution strategies and customer experience.
Known vs. Unknown risk types | Crypto
Data points are rough estimates. Building superior products requires better estimates. There is historical data in addition to new data sources (IoT devices, telematics) for known risk classes such as an accident or property. For less-known emerging risk classes it is harder to achieve sophistication in pricing and underwriting. One of the less explored fields by insurers is Crypto. Due to its decentralized nature, volatility and recent existence, there is not enough data on claims frequency or severity. Nevertheless, some frontrunners are experimenting in this field. One of the examples is protecting digital assets against financial losses resulting from hacker attacks. As a field comprising digital ownership (NFTs), financial assets (cryptocurrencies), and cybersecurity (hacks, forks) it offers ways to replicate insurance products in the crypto world, as well as create something completely new - maybe we are not yet aware of that. However, it is up to the individual risk aversion of an insurer to go into the field with many unk-unks (unknown-unknown) factors. Yet, being capable of assessing risk short-term given limited coverages enables an insurer to adapt quickly and to gain first experiences.
There is a lot more to say about data in insurance – from modelling to adapting processes around end-customers and partners to fraud detection and more. Reach out if you are interested to talk.
