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Minimising future risk through data insight

As we have highlighted previously, we live in an age where the amount of information available about people's behaviour and the environment around us is increasing daily. With the potential for anything and everything to be connected, we are moving towards a continuous stream of data from numerous different sources that can be used by those who are able to capture — and more importantly, interrogate — the information.

Minimising insurance risk through data insight

By uncovering, interpreting and communicating meaningful patterns in this data, and doing so in real time, insurers can gain unique insights about the risks they are currently writing or wish to write in the future.

Traditionally, it has only been possible to achieve this retrospectively, with insurers using what has happened in the past as a proxy for the future. While this rear mirror historical view is still important, it is just as essential that insurers keep their eyes on the road ahead to identify new patterns and trends that will impact on the performance of their business.

A good example of this is the connected home, where sensors will be able to detect whether the front door or a window has been left open and send alerts. The homeowner can then fix the issue and minimise the risk created. If this is a regular occurrence, or if the homeowner doesn't use their house alarm or leaves the house unoccupied all summer, then it will impact on their behaviour score, demonstrating to the insurer that they are a bad risk.

Combining this insight with data from other sources means insurers can understand the exact risk they are insuring and provide an accurate, tailored price for that customer rather than relying on an aggregate risk profile or demographic.

While such use of sensors may sound like science fiction, we are already seeing sensors placed in rivers and streams, as well as household appliances such as washing machines, to detect changes in water levels and predict when flooding is likely to occur. The devices send the data, and once insurers have interpreted this, they can ask what action to take or adjust the premium accordingly.

Taking this one step further, insurers can combine the data from river sensors with advance notice of impending weather events, such as a large storm in that region. This insight enables them to avoid writing any new business in the area until after the event has passed.

Other demographic data can also be used to manage risks, such as crime rates for a particular neighbourhood, the proximity to a fire station and any history of subsidence. By bringing all of this information together, insurers can assess their individual portfolio to see what their risk exposure is for a particular area. Once they have identified postcode zones where there is an accumulation of insured properties, this information can be combined with the property risk data to define an insurer's exposure in any given area.

Once insurers truly understand the risks they are facing, they can not only make changes to minimise their risk exposure for current customers, but also introduce new business models and products. By offering, for example, discounts for driving safely or being more active, insurers can attract new customers with a lower risk profile and incidence of claims.

Predictive analytics and modelling will allow insurers to visualise their data, clients, broker portfolio and risks. Using such tools, they can create propensity models (what is the likelihood the consumer will buy their product at that price) and elasticity models (how that propensity changes by increasing or decreasing the price). In addition, using data consistency models to validate the complete data set will enable alarm bells to start ringing if the Managing Director of an accountancy firm requests cover for a 12-year-old Vauxhall Corsa doing 4,000 miles a year.

Moving from reactive to predictive risk analysis in this way enables insurers to go from being a processor and payer of claims to actively preventing claims and loss in the first place. And, in essence, that is the whole purpose of digital analytics – to move insurance from a 300-year-old industry facing disruption on every front to one powered by digital insurance and technology.

SSP has the unique expert insight to help insurers make this transition so, to find out more, please contact us.

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