As the insurance market recognizes the potential of artificial intelligence, there remains uncertainty about how to effectively apply the technology to enhance customer engagement and drive sales.
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.
The use of behavioral approaches, rather than demographic-based ones, yields superior results. Responsible AI usage can help insurers attract and retain customers.
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.
According to Harvard Business Review, 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.
Known and emerging concerns associated with AI in the workplace include the spread of misinformation, copyright and intellectual property concerns, cybersecurity, data privacy, as well as navigating rapid and ambiguous regulations.
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 How Can AI Technology Change Insurance Claims Management?).
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.
To achieve a higher return on investment, it is importantly to use multiple AI models rather than relying on 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.
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 How AI Technology Can Help Insurance?).
Behavioral models can uncover differences within a customer base that demographic data alone may not show.
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.
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.
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.
Maintaining strong relationships is crucial in the insurance sector. However, neglecting this aspect may lead to customers disengaging or even canceling their insurance policies.
Propensity and behavioral segmentation models should be utilized together to address this issue effectively.
By adopting this combined approach, insurers can ensure comprehensive customer coverage, optimize the return on investment in costly communication channels, and strategically employ personalization to achieve the best possible outcomes, according to the executive.
To help decision-makers avoid negative outcomes while also remaining competitive in the age of AI, we’ve devised several principles for a sustainable AI-powered workforce.
The principles are a blend of ethical frameworks from institutions like the National Science Foundation as well as legal requirements related to employee monitoring and data privacy such as the Electronic Communications Privacy Act and the California Privacy Rights Act.
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.
Insurance customers have many possible triggers
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.
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.
Insurers should consider moving beyond the limitations of a single propensity model by integrating behavioral segmentation.
This combination offers valuable insights for interacting with the right customers at the optimal time with relevant content, ensuring consistent and meaningful experiences across all touchpoints.
Effective personalization relies on the strategic deployment of AI models. Responsible AI usage is crucial for maintaining consumer trust and ensuring the long-term sustainability of AI as a tool for enhancing customer experience.
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AUTHOR: Daniel Levy – Principal Risk Consultant Swiss Re
