Responsible use of AI critical to consumer trust: Swiss Re’s Levy

As the insurance market and its customers realise the potential of artificial intelligence (AI), it is yet not very clear how to apply the technology in a way that delivers enhanced engagement, and in turn, sales, Swiss Re’s Daniel Levy has stated.

Principal Risk Consultant Levy has recently explored how insurers can get the best results from AI-powered tools, in terms of retaining customers and improving the quality of interactions.

He highlighted the importance for insurers to leverage multiple AI models to realise a greater return on investment. He also noted that the use of behavioural over demographic-based approaches can deliver superior results as well as the responsible use of AI, among other tools, can help insurers reach and retain customers.

Levy said: “Most insurers use AI primarily to identify the customers most likely to let their policies lapse. Single-purpose propensity models are highly effective when it comes to identifying a specific subset of customers at risk of being lost.

“Applying such a targeted approach makes sense when customer interactions are relatively costly. However, if the cost of outreach is low and the subset of customers identified is a significant proportion of the total customer base then the impact of a propensity model becomes less meaningful. After all, why zero in on a subset of customers only to end up interacting with a majority of customers anyway? Conversely, why ignore most of your customers if communication is cheap?

“Propensity models may also be of less relevance when it comes to responding to situations such as inbound inquiries.”

Because of all these reasons, Levy states that in order to realise a greater return on investment, it is important to leverage multiple AI models instead of relying on the results of a single solution.

Another tool to use are behavioural models, as they deliver superior results compared to a demographic approach, the executive suggests.

Unlike demographic approaches, that divide customers by location and age for example, a behavioural approach divides customers according to behavioural patterns and formulate insights accordingly.

“In general, we have found that demographic-based approaches underperform behavioural models in terms of customer response rates. By analysing customer behaviours, behavioural models provide visibility into motivations, and allow insurers to deliver messages that speak to these directly,” Levy explains.

Adding: “Behavioural models can reveal stark differences within the customer base that may not be apparent along demographic lines.” Also noting: “Behavioural analysis can also highlight cases where customers take the same action, but for different reasons.

“Interactions that recognise behavioural and motivational differences and are tailored accordingly tend to deliver superior results.”

Levy also addressed the use of AI responsibly, remarking that even though personalisation can be possible through a type of propensity model, ethical issues need to be factored into any company’s strategy.

For example, the model could select the optimal message to send each customer from a ‘menu’ of prepared messages. Data has shown that using this method for SMS renewal messages led to a 0.8% increase in retained premiums for one of Swiss Re clients, Levy noted.

“These models can also incorporate reinforcement learning: with ongoing testing, the AI program can learn which content is most effective for each customer, as well as the ideal channels and times of day for interactions, to maximise their commercial impact,” the executive added.

“That said, these models raise possible ethical issues which need to be factored into any responsible company’s strategy. Unlike with behavioural segmentation, it is not always clear why a propensity model chooses a particular message, and the difficulty of explaining results can raise questions. For this reason their usage needs to be monitored carefully.”

The frequency of communication is another consideration highlighted by Levy. As he states that “a ‘pestering’ approach that insists on touching base every day demonstrates a lack of understanding of customers, and a lack of respect for their time.”

With ongoing relationships being vital for insurance, this kind of approach could cause premium-paying customers to stop engaging, or even cancel their policies.

Levy concluded that propensity and behavioural segmentation models should play complementary roles. This approach would ensure coverage of all customers, maximise the return on investment in more expensive communication channels, and responsibly positions personalisation to deliver the best possible outcomes, according to the executive.

The assumption that customers will either have a consistent propensity to act throughout the year, or only take action once a year, is another flaw of commonly deployed AI models.

Swiss Re has observed that customer propensity to act frequently changes. “Customers have many possible triggers, and it is important to understand what each means,” Levy noted.

He continued: “One approach which has been shown to be successful in the past is to use models to understand what individual customers may do in the next three months. By applying behavioural models to analyse past patterns of behaviour for each customer, we can understand the most likely next action each customer may take.

“Studying the behaviour of similar customers following often complex patterns of trigger events can provide insights into where a customer on the brink of a life change will go next in their journey.”

He continued: “Journeys should therefore be on the minds of insurers looking to move away from the limitations of a single propensity model. Combining behavioural segmentation with propensity models provides guidance on interactions with the right customers, at the right time, and with relevant content, to create consistent and relevant experiences across all interactions.

“While personalisation has been proven to be effective, how AI models are deployed is important. Responsible use of AI is critical to consumer trust and, ultimately, the long-term viability of AI as a tool to improve customer experience.”

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