Harnessing the potential of burgeoning data and computer power to add value must become ingrained in insurers’ every activity.
The use of data and analytics to underwrite risk is nothing new for insurance carriers.
Yet in a digital world, it is revolutionizing their business.
An industry in which 80 percent of all auto insurance claims are adjudicated automatically, and 80 percent of all life insurance policies are issued straight through without requiring any of the usual health checks, is no distant pipe dream. Neither is one in which the cost of acquiring a customer falls by as much as 70 percent because of precision marketing and personalization.
Such is the power of analytics.
The convergence of several technology trends is behind this revolution.
The volume of data continues to double every three years as information pours in from digital platforms, wireless sensors, virtual reality applications, and billions of mobile phones.
Data storage capacity has increased, while its cost has plummeted.
And data scientists now have unprecedented computing power at their disposal, giving birth to ever more sophisticated algorithms.
As a result machine and deep learning are on the horizon.
“We’re moving from computer science, where computer coders write very explicit, line-by-line instructions, toward starting to train machines to look for information that could be valuable,” says Scott Simony, head of industry at Google.Yet data and technology alone do not deliver value, as too many companies have discovered their cost. While some are seeing good results, others admit they have seen little effect to date from their investments in analytics
It is important that this changes quickly, as those slow to adopt the technology at scale will surely struggle to compete. They will struggle against other insurers that use analytics to improve their core business by streamlining internal processes, raising revenue and cutting costs in the process. And they will struggle in the longer term as data and its analysis begin to break down business models and industry boundaries.
In personal auto insurance, we can already see how data from sensors fitted to vehicles will put premiums under pressure as driving becomes safer. And we only have to glance at other industries to understand how, in a world in which data and analytics are king, powerful new competitors with large customer bases for their core businesses can rapidly invade other sectors. Chinese e-commerce giant Alibaba also owns one of the world’s largest technology finance companies, which includes among its services insurance.
Here then, is how companies can move quickly to build their analytics muscle across the organization, avoiding common problems and ensuring their investments translate into business value.
There are four phases.
Phase 1:Building insights
The starting point is to be clear about how analytics can deliver insights and add value, and choose the use cases that will demonstrate this. Too often, companies give scant thought to the business problem they are trying to solve, instead getting carried away with refining data, gleaning perfect insights, or investing heavily in technology infrastructure.It is also important to understand what analytics can and cannot do. It cannot, for example, predict outcomes with pinpoint accuracy, particularly in low-frequency, high-severity, or shock-prone lines of business. For instance, the market for directors and officers liability insurance endured waves of litigation over the past decade—and subsequent spikes in claims—resulting from events such as the financial crisis and new regulations governing options backdating. It would have been difficult to predict any of these events with analytics.