Benefits and use cases of AI in insurance

Predictions of how Artificial Intelligence can change industries – and even lifestyles – rarely fail to inspire. And we have to admit: the idea that AI could take care of some of life's tedious paperwork, allowing us to spend more time doing things we love, is a nice dream. 

When it comes to the re/insurance industry, a recurring vision for the future is that AI will potentially enable more precise coverage and pricing adjustments. While this is an attractive long-term goal, let's discuss where AI is delivering benefits today and the opportunities for the insurance value chain in the near future.

Current insurance AI applications are based on a narrow type of Artificial Intelligence. 

Narrow-AI is already being used in many industries. In insurance, it has three main functions: 

• First, it can automate repetitive knowledge tasks (e.g., classify submissions and claims)
• Second, it can generate insights from large complex data sets to augment decision making (e.g., portfolio steering, risk assessment)
• Third, it can enhance parametric products and risk solutions 

Although AI enables re/insurers to become more efficient and offer new solutions, it's important to keep in mind that, so far, an entire system is required for its use, including human interaction. The added value of AI therefore only comes from the smart combination of AI models and human processes, not just a standalone AI model. 

The AI technologies most leveraged in insurance today are Machine Learning, Natural Language Processing, and Computer Vision. As illustrated in Part 1 of our Series on "Decrypting AI in insurance", there are many governance, organisational and cultural conditions that AI applications must fulfil to benefit the insurance industry and society at large.

Advanced analytics, and some forms of AI, have been increasingly enriching the insurance value chain for several years and will have a different impact on each stage of the value chain in the future.

We use the following definitions: AI refers to mathematical models that learn patterns from data and enable faster or even automated decisions. Depending on its scope, AI can be referred to as either “narrow-AI” (models designed to fulfil a specific purpose in a defined context) or “general-AI” (a universal model with human-like intelligence). No true general-AI exists yet, but recent advances partly begin to exceed capabilities associated with narrow-AI (e.g., Open AI’s ChatGPT and GPT-4 or Google’s Bard).  

Let's look at examples in three areas:

1. Underwriting – improved risk assessment and customer understanding

Re/insurers get access to more and more data at the time of underwriting thanks to the digitalisation of existing touch points or access to new data assets with digital partners – just consider telematics, remote sensors, satellite images or digital wellness records. The ability of re/insurers to convert this data into actionable insights for underwriting, is a key competitive differentiator, as it allows them to offer customers more tailored coverage and pricing.

AI techniques such as supervised learning can complement and streamline certain UW processes – for example, when it comes to smarter triaging and routing. AI-driven predictive models at Swiss Re support triaging Life & Health underwriting and simplify the consumer journey. 

These AI models have been integrated into Magnum Pure©, Swiss Re's Life & Health automated underwriting solution platform, to orchestrate and utilise both human and machine intelligence at scale. With Magnum Pure©, this expert-backed and machine-learning-based risk assessment solution can be easily deployed across multiple distribution channels and in point-of-sales tools.

2. Claims - improved back-end processes, new products, and coverage for more risks

AI capabilities can not only improve efficiency and insights but can also enable the development of new solutions and coverage for previously uninsurable risks. Swiss Re's parametric Flight Delay Compensation is built on an AI model that can predict flight delays. In the event of a delay, customers who purchased the insurance when buying their ticket will receive an instant pay out – with no need to file a claim. The solution uses more than 200 million historical data points and the machine learning capability of the pricing engine allows for rate adjustments, based on data from over 90,000 flights per day.

3. Claims - Computer vision can reduce car accident fraud and detect driving style

Taking advantage of the confluence of edge computing and AI, an Italian startup has been granted a patent to record the front visual panorama of a moving vehicle, identify the driver's driving style, and certify the accident by recording its dynamics.

When the engine starts, the device begins recording the video and simultaneously transmits it to the cloud using proprietary technology that allows secure transmission of encrypted video snippets. Once in the cloud, the video snippets are reassembled and processed using computer vision algorithms that anonymise the personal data collected during the recordings (such as people's faces and car license plates) to comply with data privacy regulation (e.g., GDPR).

The anonymised video can then be used as evidence of accident dynamics and to extract key data to identify driving styles and the ability to classify driver risk.

The system has received funding from the Italian government to develop part of the project.

These are a few examples of data-driven AI models that have been successfully deployed. However, at Swiss Re and across the industry, many AI projects are also internally driven and address core processes – for example, using natural language understanding to help ingest and classify unstructured data into decision-making processes or to better understand the exposure in contracts and the overall portfolio.

Through the various entry points, AI has the potential to impact and add value to the entire insurance value chain and bring significant benefits to customers. However, with wider access to these powerful tools, it is also crucial to be on top of their risks and challenges. Data and responsible AI literacy are key for companies to ensure that humans remain in control of the decision-making process. 

There are many new risks associated with the use of AI at scale that policymakers, big tech companies and insurance companies need to consider working on now so that AI adds value to society and that we can still effectively protect against the new risks associated with it. And, of course, as many of us ponder the potential for AI to enhance our own lifestyles, we'd love it if AI could free up more time for some of life's enjoyable activities!