Getting personal: How AI can revolutionize personalization in insurance

Over the past few years AI has had a meteoric rise to prominence. And the financial services industry has not been slow to recognize its transformative potential. Modern insurers now realize that investing in AI is no longer a speculative choice but a necessity, with proper utilization of this technology estimated to add over $1 trillion to the insurance industry annually. 

As a result, by 2023 nearly two thirds of insurance executives had begun investing in AI and machine learning. And it’s clear that failure to incorporate AI into their business is going to leave insurers at a substantial disadvantage. 

However AI is only beneficial if used effectively. And insurers now face the challenge of how to best incorporate it into their business. Identifying and adopting the ideal use cases for AI will be vital to ensuring that it meaningfully and significantly contributes to their success. One of those optimal implementations may be using it to enhance personalization.

Potential AI applications in insurance

Our recent whitepaper lays out the possibility of sizeable improvements to a broad range of areas: 

• Policy generation – leveraging large data sets, AI can accelerate the creation of personalized insurance policies using automated policy drafting and scenario simulation.
• Risk assessment – AI provides insurers with advanced tools that support more accurate risk assessment. These can proactively cover emerging risks such as climate change.
• Customer service – this can be transformed via individually tailored recommendations and financial planning advice, enabling customers to make more informed decisions.
• Claims processing – AI integration allows for streamlined claims management, thanks to automated damage assessment and scenario analysis.

This creates an opportunity for a much more sophisticated approach to personalization than was previously possible, producing substantive improvements in customer service quality. 

Let’s look at these in greater detail.

How AI can turbocharge data analysis

AI’s ability to rapidly analyze and extrapolate vast quantities of data means that a highly individualized risk profile can be devised for each customer based on real-time analysis of factors such as historical claims data and current personal information. 

This data can then be fed into an automated policy-drafting process to dynamically price an insurance product. This price would be based on the specific individual’s needs and circumstances, rather than relying on demographic generalizations. 

The rise of embedded insurance has also added to the proliferation of datasets that can be used to dynamically generate a personalized policy for each customer. Insurers can now incorporate customer-provided data from the e-commerce process for third-party products. They can process this information using AI’s advanced algorithms and machine learning techniques to gain a deeper understanding of customers’ needs, preferences, and risk profiles. 

This increase in detail allows insurers to more effectively tailor their offering with personalized recommendations, and streamline the underwriting process.

Better performance throughout the business

This improvement to data analysis is not the only area in which AI can invigorate performance. For instance, there’s scope for transforming fraud detection. AI capabilities, like sophisticated pattern recognition, allow for swifter resolution of suspected frauds, improving the effectiveness of claims processes and significantly enhancing personalization, meeting the unique needs and preferences of each claimant. 

AI-enabled processes will also give much more support to insurance personnel. For example, AI-based document processing could transform unstructured data into an organized output, and then embed that information into claims processes. By saving time and reducing errors, this would allow the optimizing of end-to-end processing of complex claims. Claim handlers could then process claims more efficiently by making faster and more accurate decisions based on data insights. Likewise, AI-enhanced improvements to processes like First Notice of loss (FNOL) and damage assessment would also enable insurers to provide better customer experiences.

Personalized service could also be assisted by utilizing improved chatbots, setting new standards for servicing customers. Using AI-backed processes, these bots can provide each individual customer with assistance and recommendations tailored to their specific needs. However – while the process will be automated as much as possible – there will still be instances where human intervention is necessary, and a ‘Human in the Loop’ approach will need to be maintained.

The big risk of missing out on the personalization revolution

It’s apparent that even in its early stages AI has begun to change insurance, creating coverage possibilities that work better for both insurers and policyholders. Insurers can now use AI to model a much wider selection of data more accurately and more dynamically than before. This supports a raft of tools that can both streamline and enhance business – from improved chatbots to augmented fraud detection. It also underpins the creation of increasingly sophisticated and responsive personalization. 

Consumers will soon come to expect this level of personalization as standard. So it is vital for insurers to be able to meet and exceed that expectation. Otherwise they will risk being left behind by competitors who seized the opportunity. 

Webinar: The AI Edge in Insurance

Emerging trends in the insurance sector and real-world impact

Forward-looking insurers are strategically navigating the trends, challenges and opportunities to create a competitive edge and drive their businesses into the future with artificial intelligence.

In this webinar, we discuss transformative applications in insurance, real-world challenges, and transferrable learnings from other industries.

Watch it on-demand.