The important role of data analytics in value-based insurance
By Dennis Kiplang’at | August 17th 2021
One of the most renowned management gurus Peter Drucker’s most memorable quotes is “if you cannot measure it, you cannot improve it.”
In other words, what gets measured gets managed.
This business management nugget forms the basis upon which data analysis and consumption in decision-making is premised.
Also, Lean Six Sigma, the gold standard of operational methodology emphasises DMAIC - define, measure, analyse, improve and control.
For process improvements to succeed, measurement and analysis must precede, of which data is the holy grail.
Data analytics entails a review of data to garner insights and thus align the business operations to its strategic imperatives.
This process involves inspection, transformation, and modelling of unstructured data into a proper structure. This is particularly vital for the insurance industry as a risk-based business.
Data and analytics are quite literally becoming fundamental business functions within insurance.
However, despite the clear benefits, insurance companies have been slow to adapt compared to banks and other financial service providers.
But this is slowly changing, with a growing focus on business transformation, innovation, and technology.
Operational efficiencies in areas like claims and underwriting are a low-hanging fruit that many insurers are now actively harnessing.
The Data Age is providing useful trends that if well understood and captured, will unlock value for any insurer pursuing long-term business relationships.
Customer-centricity, or member-centricity for health insurers, relies on data to model product offerings that speak to the client’s needs.
This, therefore, means the conversation now is around value as opposed to cost. A prudent company will invest in both internal and external information to grasp the landscape as well as opportunities available to it.
Hence the use of predictive analytics. This is a subset of analytics that applies forward-looking models to unknown future events.
This application of analytics alongside artificial intelligence, behavioural science and machine learning, vastly enhances the ability of insurers to innovate and grow.
In addition, this might just be the engine to lift insurance penetration from the current dismal 2.3 per cent through the provision of value-based underwriting.
Data analytics comes with a trove of benefits, yet is unexplored, especially in Africa.
For instance, fraud prevention using diagnostic tools augurs well for all stakeholders by ensuring legitimate claims are paid efficiently.
Risk assessment and rating at the on-boarding stage also ensures proper risk management.
Under the precepts of lean management, internal processes can be greatly improved through insights gleaned from operational data, leading to efficient turnaround times and controls, for example using robotic process automation.
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