Guest Contributor: Ludovic Veale heads up the Data & Analytics Practice at Charles Taylor InsureTech and has over 20 years’ experience in the delivery of complex data driven projects. His extensive experience covers the convergence of relational SQL technologies with Big Data Technologies, frequently operating within Agile environments and working alongside a diverse range of clients and sectors. Ludovic joined Charles Taylor from PWC where he was a director within their Data and Analytics competency and previously at Vantage Performance Solutions, BP Shipping and Business Objects.
Connected devices, or what is more broadly referred to as the “Internet of Things” (IoT), have been a growing presence in our everyday lives. From watches that monitor our health to thermostats controlled by smartphones, these devices are increasing the amount of information available to us at our fingertips. Now, the insurance industry is using the same connected device model to alert policyholders about potential disasters. Dubbed “Connected Insurance,” it might include alerts about a fire or gas leak on your mobile device or real-time video interaction to help halt disasters in their tracks. These services rely on sensor equipment to pick up early stage disasters in the making, and can include emergency repair services that stop small-scale issues from turning into vast unwieldy claims.
The emergence of connected insurance will involve a shift from traditional ‘after the event’ policy models towards those that incorporate ways to preemptively reduce risk (and simultaneously improve the overall experience of policy holders). Selling premiums in isolation is a static market already in decline, yet connected Insurance, with loss avoidance as the key driver, will continue to grow. This brave new world of connected Insurance clearly relies on two things: good quality and timely data, yet when I review some of my client’s existing data sets this fully integrated future can seem unattainable, a million miles away. But looking around, connected insurance services are already with us and successfully on the market, so how do they do it?
Connected Insurance Demands a New Approach to Data Preparation
Under the premium-led model most data is historical and stored in traditional legacy system extracts with the usual Extraction, Transform and Load (ETL). This rigid process adequately serves standard reporting efforts— business users request data requirements from IT and, after a certain amount of time, are able to deliver insights “after the event.” With the rise of connected insurance, however, new demands are placing a strain on this approach.
Connected insurance involves more complex data feeds at a higher frequency. Predictive models via machine learning (ML) and artificial intelligence (AI) are not simply ‘nice to have,’ but rather ‘essentials’, which can add to difficulties around data capture and integration. In order to thrive in this new environment, these extra data feeds need to be processed quickly and efficiently through an agile approach that circumvents the central IT team.
Organisations that have achieved success in more quickly leveraging the complex data associated with connected insurance are those that have embraced new approaches to data preparation, such as Trifacta’s data preparation platform. Using this technology allows users to quickly incorporate additional data feeds into an end-to-end connected insurance solution, rather than having to wait and wait while the IT team incorporate new feeds and integration points within the enterprise data warehouse. These tools allow business users to add in new functionality and add-on services independently with minimum support, which of course all serves to accelerate adoption.
Real-life Examples of Connected Insurance
To see connected insurance in action, we only have to look at commercial property insurance, which is leveraging connected devices to insure an office building. Instead of issuing a standard policy, they are now including multiple sensors within the building, including fire alarms systems, carbon monoxide detectors or sprinklers, etc. in order to provide a managed service. Each sensor will play an important role in building varied and rich data sets to determine risk and, as the service matures, the ability to add on further data points and external lookups will greatly enhance available features and offerings. In order to wrangle this data, data preparation technologies are essential in making such scenarios a reality.
The same principles apply to life and health insurance, where early identification of health risks, such as a heart attack or stroke, by way of efficient data preparation is quite literally saving lives. These amazing new services, powered by data from wearables, may become so integrated with the cover itself that the consumer no longer distinguishes between the service provider of vital, life-saving data and the end insurer. Beyond the intended risk prevention, there are other, previously unimaginable uses of sensors that can have transformative outcomes. These incredible new insights opened via analytics might show us new ways to ensure a healthy and long-lived life.
The Future of Connected Insurance
In this new connected world, with sensors collecting data on potential risks, powerful real-time data flows will generate an entire host of beneficial new services, capable of providing tailored value for both individuals and organisations. The ability to control and master rich new data sets is vital to future services in connected insurance, and analytics are key to interpreting value in this brand new world. Both insurers and the InsureTechs now need to work together to successfully deliver a range of exciting new services, harnessing rich data sets and interpreting them with AI/ML, always conscious of their interdependence.
Some may be sceptical, perhaps thinking, ‘we’ve heard it all before’, perhaps recalling the advent of Telematics some years ago, when we all heard promises of how the new technology would transform the face of motoring insurance that largely failed to materialise. This, however, is a fairly typical scenario in the introductory stages of new technology, which tends to pass through a ‘hype cycle’ of enormous initial excitement followed by an element of disillusionment. However, it is in the later phases, when the data becomes fully utilised, that real opportunities materialise. In this manner, risk prevention will prove a rich area for development and growth, capable of offering far greater opportunities than motor insurance has ever seen before. We know this is the future, the way forward, and only have to look at the 300% increase in investments in Insurtech in 2017 to understand the huge appetite for this new and exciting area of disruption.
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