Number 1 – Inverse Selection
The most read article of 2022 was this one on inverse selection. Here’s how it was introduced.
“Historically, insurance contracts have suffered from information asymmetry, with the proposer knowing more about the risk they are presenting than the insurer. The latter asked questions of the former through some form of proposal, in order to reduce that asymmetry and so be more confident about how they were pricing the risk. In today’s world of digital insurance, it is possible that the information asymmetry could be reversed, through the use of all that data and analytics. The proposer knows pretty much the same as they have always known about the risk. However, the insurer has collected a lot of data points around the risk and uses analytics to gain new insights into granular risk patterns upon which to base a premium.”
Some real big implications arise from inverse selection. In underwriting, it goes straight to the heart of fairness and personalisation. It radically changes the future for claims. And it prompts some parts of the market to question whether what the proposer tells the insurer can be trusted at all. Not good for a sector striving to put the customer at the centre of what it does.
Number 2 – Is Open Data Compatible with Insurance?
Second in the most read articles of 2022 was this one on open data and its fit with insurance. It grew out of an Australian academic paper that examined the idea of open insurance and found some real problems. Here’s how I concluded the article…
“Open data is an idea that looks great when written down on the back of an envelope. Harsh perhaps, but true when considered against the context of three things: the terms under which it will be offered to consumers ; the market practices and principles that insurance operates under, and ; the obligations of insurers and consumers when entering into a contract of insurance. What this paper exposes are the tensions inherent even at the principles level, between open data and insurance practices and obligations. People are fond of referring to open data making the insurance market more open, when it will in fact do the opposite. On top of that increased opacity, it will exacerbate the problems that people who are vulnerable or less able to engage with the digital economy, currently face.”
Again, this is an ethical issue (transparency) that is being exposed through some of the big trends in how the sector is proposing to use data and analytics. It requires significant investment and so produces an obvious question about how this is being protected by simple things like ethical risk assessments.
‘Open Finance’ is a big trend at the moment. It is also founded upon some fundamental principles. If those fundamental principles conflict with how insurance works, as seems likely, then the following dilemma emerges: do you take away the principles underpinning open data, or take away the access of insurers to open data? If a middle ground is sought, how is that then brought about?
Number 3 – Controls for Insurers’ Use of Social Media Data
The third most read article in 2022 looked at how insurers are using social media data and what sort of controls they should have in place for that. Sometimes the sector has got it wrong, both with platforms and the public, so controls of some form are pretty important.
Insurers often base their controls around legal obligations, which is fine on one level (the law is important), but dangerous when reputation and trust suffer (ethics is important too). Ethics is much wider than the law, and is something that influences the public in more ways than the sector often likes to acknowledge.
In the article, I look at developments around the collection and use of social media data for understanding someone’s health and emotional state. And from this I set out seven areas in which controls are needed.
The core ethical issue involved here is autonomy and I end the article with this reminder for insurers…
“We know that people often present themselves in differing ways on different social media platforms. So for example, how my eldest daughter presents herself on Linkedin is different from how she presents herself on TikTok. All of us, from time immemorial, have presented different aspects of ourselves in different circumstances. Creating one identify from social media data scraped off different platforms ignores this. Yet if that one identity then influences decisions for that person’s policy and claim, those differences will be ignored. Clearly, that’s not good for the quality of automated decisions.”
I’ve often heard insurers talk about social media data in terms of ‘if it is out there, what is wrong with using it’. As a simplistic way of thinking, it’s difficult to beat. Sometimes firms need to do better, for financial as well as product and customer reasons.
