We live in a world afloat with data, in which almost every item of technology leaves a wake of gathered information about us. Most people are aware of the data produced by personal fitness devices and smart phones tracking our movements, browsers collecting our search habits, and vendors tracking our spending – but few are aware that a single Boeing 787 flight typically generates half a terabyte of data. It has been estimated that, in the near future, internet-connected cars will each send 25 gigabytes of data to the cloud every hour as they drive us around.
An interesting phenomenon of this century has been our willingness to give such personal data away for very little in return. In exchange for constantly giving our location to phone providers, we receive “Find Your Phone” apps. For the pleasure of free online searching, we give Google all our search and online shopping histories. And for free access to social media platforms, we are willing to share a great deal about every aspect of ourselves and our networks. These seem like small returns for sacrificing our valuable privacy – but how valuable actually is that privacy?
Unsurprisingly, risk financers such as insurance companies have been early adopters of mining big data and of the artificial intelligence (AI) that gives it meaning. Much has been written in the media about insurers starting to grasp the value of large data sets, and many are investing in the artificial intelligence needed to make those data hoards useful for understanding risk. For example, QBE is a leader in this area through investing in AI companies and developing machine-based learning platforms for analysing policy wordings.
Insurers say that there are significant potential benefits for both customer and underwriter from using AI and big data to better understand risk. Such insights could lead to improved policy wordings and more accurate premium assessments for risks, as insurers will have multiple sources of data from which to ascertain a risk, and won’t need to rely solely on what the applicant reports.
Brave new world
So, what will this future look like? In the next few years, insurers will develop better computing abilities and AI capabilities to improve risk modelling. At first they will apply these tools to their own data sets, such as underwriting applications and claims experience, but it is likely that others with large data sources will soon avail themselves of such tools, whether through direct purchase or by investing in technology. For example, a New Zealand company, Rocket Lab, recently put three commercial satellites into orbit via rocket launch – a service it expects to cost just US$4.9 million per flight. Such cheap launch technology, when mixed with the recent development of low orbit nano-satellites, will allow insurers to gather geographic and surveillance data at reasonable costs. It is not much of a leap to then envision insurers tracking our smart cars via their own satellite networks in order to better understand policyholder driving habits.
What about keeping “risky” clients under observation? For example, such nano-satellites – no larger than a mobile phone – could also be used by insurers to keep an eye on developments in flood-prone regions. It is beneficial for local councils to develop areas that are attractive to high-end home buyers, but these areas – which often include waterfront areas, river view lots, or expansions into flood zones – can be risky to insurers. While the council might promise mitigations such as improved drainage or levees, such works don’t always keep pace with the lucrative developments of coastal and other waterside communities. With inexpensive space-based surveillance technology – and the AI to capitalise on the data streams it generates – insurers will be equipped to change the way in which products are sold and underwritten.
A future of disruption?
There looms, therefore, the very real possibility that the insurance market as we know it will experience significant disruption as a result of this profusion of personal data meeting advanced AI. While insurers continue to explore the help of these new resources in improving underwriting and the accuracy of forecasting models, such technology, once loose, tends to be adopted by all parties, not just its inventors.
From the insurers’ perspective, they are likely anticipating that consumers will consider providing personal data – such as fitness tracking from a device – as an advantage if leads to better coverage at a lower price. Other relevant information might include applicants’ smart home readings, smart car printouts and even smartphone records. In exchange for relinquishing this privacy, consumers could receive substantial premium savings.
Experience teaches us that technological advances can cause interesting disruptions. New technology sometimes goes in unanticipated directions – just ask taxi drivers, small hotel operators or bookstore owners. Each of these have been disrupted by new computing software. If we acknowledge this sea of public and personal data as the momentous resource that insurers believe it to be, then other parties will be quick to follow in recognising its value and using it to change traditional customer/client relations
By Harry Rosenthal, General Risk Manager.