Ideas & Debate

Data analytics remains key to value-based insurance sector

big data

Data analytics can help insurers overcome some of the challenges that an uncertain future brings. FILE PHOTO | FOTOSEARCH

Summary

  • Unlocking value requires a paradigm shift from traditional tools, which are more hands-on to automated analytics tools. Further, these should not be managed as IT projects but owned by the operations/management function of the business.

One of the management gurus Peter Drucker’s most memorable quotes is, if you cannot measure it, you cannot improve it or put simply, 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 define, measure, analyse, improve and control (DMAIC).

For process improvements to succeed, measurement and analysis must precede, of which data is the holy grail. Data analytics entails a review of information 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. This is, however, slowly changing with the growing focus on business transformation, innovation, and technology.

Operational efficiencies in areas such as claims and underwriting are low hanging fruits that many insurers are now actively picking. 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 need. 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 percent through provision of value-based underwriting. Data analytics comes with a trove of benefits yet 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 onboarding 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.

Straight through processing (automated insurance processes) based on value stream mapping (analysing current state to design the future) is another possibility but this requires proper, concerted analytics.

Investing in analytics should be a priority in the strategic planning and investment phases of each insurer, coupled with primed data monetisation methods.

This is still at an early stage in the insurance industry but will soon evolve into an imperative for success. To use a common phrase, it will be the industry sword of Damocles. The future calls for proper resource planning and investment right from training to institutional capacity building.

Unlocking value requires a paradigm shift from traditional tools, which are more hands-on to automated analytics tools. Further, these should not be managed as IT projects but owned by the operations/management function of the business.

Whether an insurer begins a process transformation with small-scale experiments or dives in on a larger scale, deployment of advanced analytics in a decision process is a complex undertaking demanding a thoughtful approach in multiple dimensions.

With the Data Protection Act in place, regulatory compliance on information acquisition and handling is mandatory. Soon, data-driven insurance solutions will become the norm rather than the exception.

Dennis Kiplang’at is the Head; Operational Excellence and Analytics at UAP Old Mutual.