“The acceptance of these advisors can in part be attributed to the increasing sophistication of algorithms used and machine learning. Thanks to the ubiquity of data, machine-learning has become more efficient across all industry sectors. With more input channels, more connected devices, and the influx of real-time analytical solutions, Artificial intelligence (AI) is a more reliable offering than what it was even two years ago. This has seen many routine administration functions being taken over by these systems, freeing up employees to deliver more strategic value to the organisation,” says Stuart Blyth, director at SilverBridge.
A robo-advisor is just a sexy moniker used when financial planning meets AI. However, the evolution of AI in recent times have made it possible to incorporate more personalised options for organisations. When it comes to the insurance industry, customer expectations are built predominantly around this bespoke approach to product or service offerings.
“This is why it perfectly supplements what insurers are doing today. The market need is one which requires customised solutions for competitive differentiation. While some insurers are using these robo-advisors for capturing menial information such as customer details and risk profile questions, the potential is much more than that.”
“While cynics might baulk at trusting machines to manage their insurance, the reality is that it is already happening without many people even realising it. Even though the degrees of adoption might vary per insurer, the role of AI in data analysis is a fundamental aspect of the digital transformation of insurance.”
So, despite its trendiness, robo-advisors have been used as the means to complement existing service offerings for a while. What is new is the extent at which insurers are open to using these as tools to enrich the data they have at their disposal and further customise products according to specific customer needs.
Some fear that robo-advisors might have peaked too soon with several well-known international ones closing. However, as with any other technological innovation, it is advisable to manage adoption in digestible customer chunks.
“In other words, instead of embracing all facets of robo-advisory, insurers should take elements and slowly roll out additional functionality to their solution stable. In this way, they are able to mitigate some of the risks typically associated with ‘bright new toys’. So, instead of giving consumers all the bells and whistles when it comes to robo-advisory, they gradually phase in elements of it to acclimatise customers to its benefits.”
Digital consumers are certainly more open to the benefits of enhanced offerings delivered using machine learning. At what extent the insurer is ready to provide those is up to the organisation.
