How insurers can optimize the use of AI-powered tools?

Swiss Re examined how insurers can optimize the use of AI-powered tools to retain customers and improve interaction quality. They highlighted the importance of leveraging multiple AI models to achieve a higher return on investment.

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.

The competitive nature of AI development poses a dilemma for organizations, as prioritizing speed may lead to neglecting ethical guidelines, bias detection, and safety measures.

To mitigate these risks, we propose thirteen principles for responsible AI at work.

The presence of copyright and intellectual property infringements, coupled with the legal implications of such violations, poses significant risks for organizations utilizing generative AI products (see AI Technology Change Insurance Claims).

Using an AI-targeted approach

Using a targeted approach is practical when customer interactions are relatively expensive. If outreach costs are low and the identified customer subset is large, the impact of a propensity model diminishes. Additionally, propensity models may be less relevant for responding to inbound inquiries.

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.

Swiss Re 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 (see AI Technology Helps Insurance).

Tailoring interactions to recognize and address these behavioral and motivational differences generally yields better results.

Summary of Demografic vs Behavioural segmentation

Behavioral analysis can identify instances where customers perform the same action for varying reasons.

Using AI responsibly

While personalization through propensity models is possible, companies must consider ethical issues in their strategies.

For example, the model could select the optimal message to send each customer from a ‘menu’ of prepared messages.

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.

Insurers can improperly use licensed content through generative AI by unknowingly engaging in activities such as plagiarism, unauthorized adaptations, commercial use without licensing, and misusing Creative Commons or open-source content, exposing themselves to potential legal consequences.

With the rise of Generative AI, more than ever before, organizations need to think about building AI systems in a responsible and governed manner.

To ensure that AI serves as a valuable tool rather than a potential hazard, it’s not only crucial to adopt a framework for responsible use, but acknowledge the key role we play as the users of that technology.

Guiding principles for AI

The 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.

Propensity and behavioral segmentation models should be utilized together to address this issue effectively.