How to transform insurance through big data

Big Data

Trends can be identified through various data sources such as medical history, lifestyle choices, demographics and even driving habits to ensure a correct pricing structure.

By FRANCOIS VAN DYK

Every now and then, a new buzzword pops up which remains fashionable for a while but then disappears again. “Big data” does not appear to be one of these terms. Many executives, entrepreneurs and marketers have now adopted it as part of their jargon – but the reality is many use the term without knowing the true meaning of it. Wikipedia defines big data as a “broad term for data sets so large or complex that traditional data processing applications are inadequate.”

Big data is seen by most as a result of the modern digital world where everything from mobile phones, weather sensors, social media updates and banking transactions create huge masses of data. Some scientists currently estimate that with current growth rates, the amount of data in the world roughly doubles every two years. By 2015 some estimates believed that 90 per cent of data in the world was created in the preceding two years! So yes, the digital world has massively impacted the growth rate of data.

The “information explosion” (a term first coined in 1941) has however been around for much longer than what most think. A Wesleyan University Librarian, Fremont Rider, published his The Scholar and the Future of the Research Library in 1944 and estimated that within a century Yale University would need 10,000km of shelves to store around 200 million volumes!

“The rate at which we’re generating data is rapidly outpacing our ability to analyse it,” Professor Patrick Wolfe, from the University College of London’s Big Data Institute, told the Business Insiderin 2015. Wolfe further claims that only about 0.5 per cent of all this data is currently analysed. “The trick here is to turn these massive data streams from a liability into strength.”

The insurance industry has transpired as one of the pioneers transforming masses of data into insights to improve its business. A specific branch of data analysis, predictive analytics, looks at historic and current events and data to try and predict future events or behavior. As insurance companies guarantee financial compensation for specific losses, damages or even death, it always searches for ways to predict the future to minimise their own risk.

Some global insurance players are already tapping into innovative ways to combine different data sets to improve their service offerings.

Combatting fraudulent claims

The US-based Coalition Against Insurance Fraud, a grouping of various insurance companies and other government and consumer bodies, estimated that USD80 billion was lost to fraudulent insurance claims in 2006 alone. Some studies suggest that up to 10 per cent of all insurance claims are indeed fraudulent. This comes at great cost to the industry and clients. With access to big data, insurers can now combat fraud by profiling claims against historic claims which proved to be fraudulent.

 Trends can then be identified and in combination with additional information such as social media and demographic data, a much better profile of possible fraudsters and their habits can be constructed. Hence, it becomes easier to red-flag suspicious behaviour for closer scrutiny.

Better pricing for insurance premiums

A KPMG East Africa Insurance Fraud Risk Survey in 2015 found that fraud increased the average insurance premiums in Kenya by up to 25 per cent. Should fraud be curtailed, this could already mean a big saving for insurers and their clients. In such a competitive industry, it is critical to ensure that premiums fit client budgets and big data enables a much better view of the specific customer and associated risks. Trends can be identified through various data sources such as medical history, lifestyle choices, demographics and even driving habits to ensure a correct pricing structure.

Discovery, the South African insurer, has developed the DQ-Track app which monitors driver behavior and calculates a monthly score based on factors such as acceleration, speed, braking, night time driving, smartphone activity and even cornering. The mass of data collected via this app gives this insurer a treasure trove of valuable information. Motorists who drive responsibly are then also rewarded with a point which not only improves their risk profile but also gives them access to other benefits such as vehicle service discounts and fuel refunds.

Improved marketing and competitiveness

A recent big data survey by IBM found that 74 per cent of insurance respondents reported that the use of data and analytics is creating a competitive advantage for their businesses – this being an increase of 111 per cent from a similar study they conducted in 2010. Though competitor intelligence have always been important for marketing and communications decision making, the abundance of new data through social media and more traditional media monitoring and analysis is creating staggering opportunities for marketers.

Combining traditional media data with consumer insights, financial data and social media analytics will help insurance businesses to maximise their marketing and reputational activities to outperform competitors. Opportunities in the market can easily be identified and strategies amended accordingly. More effective channels of communication can be used and closer relationships built with clients and target audiences.

There are many free tools available to assist marketers. Google Analytics, Facebook and Twitter analytical tools provides great insights to identify target audiences and marketing opportunities. Many new tools are also being launched to track masses of data and provide relevant insights. A great example of these resources are http://www.africabrandindex.comwhich rates big brands in Kenya, Nigeria and South Africa on their social media performance – a hit parade looking at various industries, including insurance brands.

The site looks at key social media metrics across Facebook, Twitter, YouTube and Instagram and scores are provided for areas such as social media growth, content, responses and sentiment.

Each month, the scores are updated, and tracked as they change over time. This enables brands to independently track their social media performance against their competitors.

The biggest challenge when it comes to big data is really combining structured data, which is data residing in structures such as databases or spreadsheets, with unstructured data – emails being an example. Unstructured data could be hugely valuable but it is very time consuming to sift through this and compile it into an easily searchable system to extract insights.

The amount of data will grow exponentially as the internet of things becomes mainstream. Data has always been the insurance industry’s most powerful resource and the spoils will go to those who find effective ways of mining big data – but it is critical to ask the right questions in order to get the right answers.

Francois van Dyk, @sbalie, heads up Operations at Ornico, the Brand Intelligence research company. He studied and taught public relations before entering the world of media research.

Email: francoisv@ornicogroup.co.za

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