The rise in predictive analytics is revolutionising the insurance industry by enabling savvy insurers to predict risk
Dramatic advances in artificial intelligence and machine-learning technologies have accelerated the ability of insurers to predict risk. Algorithms can find trends and patterns that help forecast the probability of a risk situation occurring again.
By utilising internal and external data sources, algorithms are selected according to how a specific model fits with the insurer’s data. This model is applied to predict or detect the likelihood of an event happening, such as a person needing medical attention abroad for travel insurance or a house flooding for home insurance.
Insurance and assistance provider The Collinson Group uses a variety of predictive analytical tools to flash through terabytes of data to find variables, some of which it hadn’t considered, to help predict customer risk and purchasing behaviour.
With this technology, the company is able to identify fraud and the different networks of fraudsters acting in the market, as well as increase its understanding of customers, and ultimately tailor its offering to provide them with better products and services.
“Predictive analytics has enabled a more scientific approach to analysis, allowing us to analyse more data in little or no time, and to explore parameters and factors we could not have identified with the human eye,” says Jean Ortiz-Perez, the company’s head of analytics. “The concept and objective of what we do have not changed, but the mechanisms and techniques are now much more sophisticated.”
The role of predictive analytics in insurance can actually be traced back two or three decades in the area of natural catastrophes and climate. Analysis of 50 years of data on hurricanes, for example, has proven extremely powerful in terms of helping insurers to predict future hurricane behaviour and its likely impact.
However, this has required a large amount of human input and oversight. More recently property and liability insurers have been playing catch-up with the life insurance sector, where a rich trove of available data, including longevity, gender, country and quality of life, has allowed for clearer analysis and confident predictive outputs.
The rapid evolution of machine-learning capability and the wider availability of data through connected devices are now set to make the use of predictive analytics ubiquitous across the industry. And to accelerate the necessary collection of data, new health insurance models are emerging that actively link premiums to analytics.