Modernising analytics in insurance: How Santam is changing its playbook

3 min read 14 February 2022

Insurance has been a fertile proving ground for analytics over many years. Underwriters have long used models to improve forecasting and predictions. More recently, insurers have started to use analytics to track and respond to client behaviour, to improve client-centricity, build lasting relationships and ensure positive outcomes for all parties involved. The proliferation of modern data analytics within the industry has had a profound effect on how we do business. It has given us insights that help us understand our clients better. If we want to continue to reap the benefits of these insights then we will have to make sure we effectively harness the power of data.

Increasing Engagement Across Organisations

I am pleased by the headway we are making when it comes to modernising and becoming more insights driven. A key factor behind this has been the improvements in hardware and software processing power over the past few years. For example, thanks to the ubiquity of facial recognition software on smartphones it is easy to forget just how much processing power goes into simply unlocking our phones. We are taking that incredible computational potential and aiming it at generating deep and useful data that can help improve our clients' lives.

One of the essentials of becoming data-driven is an understanding across the organisation that data-driven insights are important. It is also vital that people understand that the insights are gained from analytics, and therefore that analytics adds value. At Santam, there’s a deep appreciation for this. We’ve ensured that we’ve got a seat at the table for most of the discussions where analytics may be relevant. We’ve packaged analytics to make it attractive and understandable. We’ve focused a lot on storytelling and showing people how analytics can help; for example with developing personas and segmenting customers, and then how they can use that information to understand the value of particular groups or subgroups and make decisions about priorities on a more granular level.  This creates for a more personable experience when interacting with our customers.

Speeding Up Processes

Often the best examples of how analytics can generate value for businesses are the simplest. At Santam, one good example of generating value through insights has come from joint ventures. The ability to describe your customer portfolio in a meaningful way often poses a challenge when discussing potential value during the pre-inception stage.

When we have considered ventures with external parties in the past, the conversation about customers tended to be protracted. Now we can give a clear profile of our customers within a week or two and be able to quickly see where there is overlap, and where there might be opportunities for collaboration. This is a massive advantage in speeding up those engagements.

A Journey Or An Ending?

There is the question of whether the modernisation of analytics can ever be ‘complete’. In other words, is it a journey or does it have a defined end? We believe it’s an ever-evolving journey that is changing over time. At present we are very much in a transition phase. As we shift more toward machine learning, the line between person and machine will continue to blur and this will have an effect on how we do business.

For example, the way we collect data now is very conducive to hyper-personalisation. This is good because it allows us to treat each client as an individual and provide tailored solutions. Adopting a hyper-personalised marketing strategy powered by data analytics and AI gives us the insights and capabilities to adapt to client needs in real-time. That said we will have to be mindful and ensure that hyper-personalisation doesn’t become invasive.

Over the next five years or so, our business is going to change, and we will continue improve the ability to convert the minimum viable insights required as we ‘level up’.  Take for example how we may adapt to the potential wide-scale use of autonomous vehicles. If you are travelling in an autonomous vehicle and it skips a traffic light and has an accident, who will be liable? Perhaps a solution will be to offer liability insurance for AI / Computer Vision software licenced or developed by the vehicle manufacturer. 

From an analytical point of view, one of our challenges is how we integrate with our legacy ecosystem. We have a lot of intelligence in terms of decision making happening across the organisation, but right now, roughly 70% of it is still done manually. There’s a huge demand to automate that, and that’s the main area of work for us right now.

Another example of integration between person and machine, or device, is the services becoming more prevalent on smart phones.  Very soon we will get to a point where a agent could do a full assessment or bind a policy using just your phone. We may reach a stage where a policyholder, provided they aren’t injured, can do an accident assessment themselves simply by uploading and processing the relevant images.  On-demand insurance is already a reality, with the likes of JaSure, enabling customers with smart phones to work through the binding processes without any human interaction.

Modernisation, requires more than just updating technology. It's very much a change in behaviour and a shift in mindset. Many people avoid engaging with analytics because they suspect it’s going to be beyond their capability.  This makes change management and mentoring a critical part of the transition. Mentoring and coaching makes up around 30% of the journey with another 20% being the ability to access the relevant data. Another 40% is about having the right ecosystem and the final 10% is about tenacity. Get those right, and you can encourage people to experiment. That’s how you move your organisation forward.