In the dynamic landscape of the insurance industry, one might wonder why insurers seem to be engaged in a guessing game. As risk factors evolve, customer expectations shift, and global uncertainties persist, insurers find themselves grappling with challenges that require them to make informed decisions. Too many insurers claim to deploy modern, cutting-edge technology solutions yet their systems are built to process actions without using the valuable insights that can improve the user experience.

One of the key factors contributing to the perceived guessing game is the complexity of risk assessment. Insurers operate in an environment where risks are multifaceted and continually changing. From natural disasters and economic fluctuations to emerging technologies and evolving customer behaviours, insurers must navigate a myriad of variables to accurately assess and underwrite risks.

The guesswork is left to the staff using the system, who often make their own subjective decisions as the system provides no insight, despite the volumes of data available to it.  

The question that needs to be asked at the start of a system design is whether those setting up the processes are equipped with the skills needed to ensure that the system “learns” from the current customer journey and is capable of learning from the process afterwards.  In most cases, they are not! As a result, the process is not as efficient and effective as it should be.

It is not always obvious to those setting up these systems that a clear differentiation needs to be made between the “actions” part of the machine and the “brains”. There is a difference between just “doing” and “processing, learning improving”.  

The challenge faced by decision-makers tasked with implementing machine learning in their processes is identifying developers who can do two things –build a process and enable it to evolve. Many smart developers are great at creating task and goal-orientated processes, but developers who create systems to analyse, observe and optimise have a completely different skill set – this requires a fusion of data science and technology.

For optimum productivity, data will be fed into a decision engine, models will be developed, and algorithms will run, whilst simultaneously feeding back fresh insight into the system, suggesting real-time changes to the use experience such as new product offerings, personalised incentives, useful marketing messages and improved claims handling.

If systems lack inbuilt data science capabilities, insurers will need to bring in external resources to analyse the data, often using (delayed) offline processes and with higher human costs and biases.

This can compromise the customer journey and reduce efficiency. Sales will be lost due to inappropriate focus, claims will be mismanaged due to routing delays and lapse rates increase due to inefficient retention strategies. The actual cost to the insurer ends up being much higher.

Decision makers who recognise the need to design systems to learn will shorten the insight loop by appreciating the value that real-time business intelligence can add. They benefit from thinking systems as opposed to processing systems. Furthermore, insurers are increasingly integrating data analytics and machine learning into their operations to enhance risk assessment. While these technologies hold great promise, their deployment is not without its challenges.

In the ever-evolving landscape of the insurance industry, the perception that insurers are playing a guessing game is rooted in the inherent complexities they face. To be competitive, insurers should be asking how smart their systems are, and whether their processes learn and improve over time. If the answer is “not really”, the guessing game will continue.

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