Predictive analytics vital for personalised insurance

Predictive analytics vital for personalised insurance
Kelly Preston, data analytics manager at SilverBridge
Predictive analytics vital for personalised insurance
Kelly Preston, data analytics manager at SilverBridge

Having access to sophisticated predictive analytics has become a business priority for insurers. Being able to factor in historical and real-time data to analyse patterns, determine risk, and identify trends is now fundamental to insurance success. Kelly Preston, data analytics manager at SilverBridge, discusses its importance in the digital age.

“The insurance industry is no stranger to analysing data. In fact, underwriters have used it to evaluate the risks of insuring a person, company or item to determine the appropriate premium before a policy is issued. However, the traditional way of doing this has been limited to a siloed approach that does not factor in all data points,” says Preston.

These data points have grown significantly in recent years thanks to the arrival of social media, connected devices, artificial intelligence, and other innovations. Today, insurers must be more cognisant of the plethora of platforms customers use and engage with, be able to capture that information more accurately, and analyse it in the broader context of a business environment.

Bespoke development

“Customer-centricity might seem like a trite term thrown around by marketers, but predictive analytics empowers an insurer to do just that. Taking the time to scrutinise a broader cross-section of data points, understand its relevance to customers, and see its potential impact on the industry in general, might mean the difference between business growth and stagnation,” she says.

People are now used to how certain services collate information on them in exchange for being able to use the product for free. Think the likes of Google, Facebook, and Instagram. There is a saying that if something is free, then you are the product. These companies use the data to either sell it on to third-parties or develop more bespoke solutions that can be delivered according to the unique requirements of an individual.

“For insurers already adopting a data collection mindset, it does not require a significant leap of thinking to expand its sources and leverage the insights gained to deliver more bespoke offerings. Intelligent insurance management platforms are now available featuring enhanced dashboards which deliver a virtual abundance of insights to product developers and decision-makers.”

This results in a more personalised approach that creates a customer experience that is more personalised than what was previously offered.

Behaviour analysis

Another important component of predictive analytics is how it provides an insurer with more nuanced knowledge into the state of the market. Leveraging more data means that more accurate customer behaviour can be predicted which can improve everything from marketing and sales, to customer service delivery. Given how influential insurtechs have become in taking clients from traditional insurers, this could be a defining reason to embrace predictive analytics.

“It is also important to note that when we refer to prediction behaviour, it is actually forecasting on what might happen and not something that is necessarily cast in stone. Just like any other technology innovation, predictive analytics should not be viewed as a silver bullet to solve all the industry problems of insurers. Instead, it is about using it as an additional tool in an ever-growing armoury of solutions to deliver products to customers in a digital environment,” she concludes.


Edited by Daniëlle Kruger

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