The Internet of Things, satellites & drones, and advanced data analytics are being put to new — and profitable — purposes in insurance. We explain what these technologies are, and how they are being applied.
The technologies used by the insurance industry have changed remarkably in recent times. Advanced data analytics, satellites & drones, and the Internet of Things (IoT) are no longer science fiction: they are being broadly applied by insurers globally. They hold enormous potential for improving data collection, streamlining claims, lowering the cost of underwriting, and preventing fraud. Here we review some of their current applications.
Internet of Things (IoT)
IoT technology consists of ways of collecting data via sensors & computer chips, and sending data back to insurers or other companies via the Internet. These technologies are being used for many insurance purposes, ranging from personal, car, and household insurance, to property assessment and weather prediction.
Connecting devices and sensors to the Internet allows insurers to collect far more accurate data than in the past. It can also save consumers money and help them to find insurance products that suit their lifestyle. Examples of use cases include:
- Wearable technologies that can lower the cost of your health insurance
- Household appliance connectivity to offer more tailored insurance
- Black boxes in cars can lower premiums for younger drivers
- Using weather data to generate alerts so customers can protect against damage
However, the IoT still has some way to go. Many people are concerned about the risks IoT generates for our privacy and security. Not all drivers are willing to install black boxes in their cars, according to the Financial Times. At home, some of our appliances are already connected to the Internet, but it is likely to be some time until we live in true “smart homes,” partly because many people do not trust the technology, and also because regulations impact what is permitted.
Drones & satellites
Satellites have long been used to map out property borders and assess damage, especially from natural disasters. Drones are now being used extensively for similar tasks, and tiny satellites are set to further improve data collection and reporting.
Compared with ordinary satellites, drones and tiny satellites are far cheaper, more mobile, and can be used to collect fine visual details. With drones, companies can sometimes do the work themselves, rather than having to hire specialized companies. Drones are also much easier to modify, such as adding on new sensors (e.g. depth sensors).
Drones and the sensor technologies built into them are helping insurance companies to write more tailored policies, provide coverage more speedily, and assess insurance claims post-event. Examples of use cases include:
- Using satellite imagery of vegetation to improve index insurance
- Using satellite data to improve livestock insurance in east Africa
- Deploying drones to inspect individual insurance claims
- Mass inspection of damage after a natural disaster
The use of drones and satellites by the insurance industry is not without drawbacks, however. Even though the capital outlay for the equipment is lessening day by day, the implicits costs can be high, especially when companies must hire skilled employees to use them. Drones are also subject to changing regulations, and can potentially open up lawsuits regarding privacy invasion or accident claims.
Data collected via consumer behaviour on the Internet, the IoT, black boxes in cars, drones, and many other methods is of little use without analysis. This is where data analytics comes in. Data analytics refers to a wide range of tools and methods for analysis and visualisation.
Insurance, which depends upon highly accurate risk calculation for underwriting, especially benefits from analytical tools and techniques. Improved data analytics are helping to make better risks calculations, thereby lowering both companies’ costs and the premiums offered to customers. Examples of use cases include:
- Analyzing enormous data sets in real time using in-memory computing
- Predictive analytics for underwriting and pricing insurance
- Robo-advising uses advanced analytics and cognitive computing to give consumers insurance advice
- Counterfactual risk analysis, which models how an event, such as a disaster, might have turned out differently, to improve predictions for future events
Because data analysis for insurance is complex, it is generally carried out by highly-trained professionals using specialized software. Let’s Talk Payments have compiled a list of the 80 hottest insurance tech companies, and the Insurance Technology Guide also provide a database of insurance companies and services.
By Erin Taylor, Senior Researcher at Holland FinTech
This article is part of a series on InsurTech. Other articles in this series include Insurtech essentials: what you need to know, Insurtech trends: towards 2018, and Insurtech in peer-to-peer insurance.