Tower Street, London, 1688. An unremarkable coffee house in London, called Lloyds, emerges as a meeting point for merchants, sailors and ship owners seeking to organise maritime insurance. Lloyd’s goes on to spawn a market worth $5tn in insurance products and solutions. Today the global insurance sector is on the brink of another wave of sweeping change arguably as profound as 1688. Only this time it is caused by disruptive technologies rather than trade.
A single word – data – explains the big change facing the insurance industry. The amount of data available to quantify insurable risk has grown exponentially in the last 30 years. In fact, by 2020, the data we create and copy annually will reach 44 zettabytes. The difference now is that the computing power to process this data has now caught up. Consider, for example, the ability to sequence a human genome in as little as one to two days – when sequencing the first human genome took 13 years to complete.
Three areas of insurance set to change
Today, sensors embedded in ships, machinery, homes, cars, buildings, infrastructure and wearable devices are generating terabytes of data about behaviour, conditions and events; the three crucial ingredients for calculating risk. Artificial intelligence (AI), specifically machine learning, is finally capable of handling the volume of data produced by these sensors, which is beyond the comprehension of the human mind. Combined, the Internet of Things (IoT) and AI will drive change in three important areas of insurance:
In 1688 the risk premium for maritime shipping was determined by the route and the value of the cargo. Even today this remains a common formula, yet a constant flow of data from modern ships, weather readings and even crew wearables is heralding a shift in emphasis from risk mitigation towards risk prevention. In a world where risk is actively reduced, old fashioned insurance business models will die. Expect to see consumer insurance companies emerge as ‘life partner’ businesses.
Today car insurers, such as Drive Like a Girl, offer personalised premiums based on telematics data, whilst companies like Cuvva offer on-demand insurance for infrequent drivers. The possibilities for personalisation are based on the millions of data points now available about individuals, which in turn enable more accurate predictions about future events such as accidents, injuries, deaths and claims. Expect to see insurance companies offer personal virtual risk advisors.
Paperwork has been the mainstay of insurance for the last hundred years, often to the frustration of consumers and employees. Optical Character Recognition (OCR) technology and AI offers a way to intelligently review thousands of documents and process claims at a far greater speed, as well as to identify patterns hidden in the data, such as fraud. The same technology allows consumers to be on-boarded faster and more efficiently. Expect to see claims processed by virtual agents with web and mobile interfaces replaced by voice.
Digital plate spinning
These changes must be embraced by the insurance industry. Currently, insurers count on less consumer trust than their counterparts in manufacturing, retail, and even banking – largely due to claim inaccuracies resulting from human error. Data can help insurers make more accurate decisions about risk premiums, offer a greater level of personalisation than any other industry, and in tandem with automation, can even make insurers more efficient and accurate. There’s a demand for data-driven change – a third of UK consumers would happily share their social media data with insurers if it meant lower premiums, for example. If the insurance industry is to restore consumer trust, making better use of data would be a good place to start.
Ultimately, while coffee is little changed since Edward Lloyd’s time, the insurance industry is about to become unrecognisable. As we enter a new phase of the digital revolution, driven by advances in AI and the IoT, insurers face the daunting task of managing existing (and it should be said profitable) business models, whilst simultaneously building alternative (disruptive) models using technology. If they can manage this successfully they will have pulled off an extraordinary feat of risk prevention and renewal.