“Alexa, can you reorder toothpaste, get bottled water, and purchase a 20-year, $500,000 term life insurance policy?”
OK, we’re not there yet, but there has been a significant evolution in the application of artificial intelligence (AI) and machine learning within the life insurance industry.
It’s a natural fit for the capabilities of AI because of the large, complex data sets with nuanced relationships, years of historical data, and a unique sales process in need of a facelift.
AI is most commonly seen through the lens of natural language processing (NLP) — NLP takes over when someone asks Alexa for insurance quotes, interacts with social media chatbots, or even files an insurance claim.
In the pre-purchase education phase, AI bots could be used to help people understand their insurance needs, answer questions about their financial situation, and help customers continue with confidence down the path to purchase. It must be highly sophisticated and personalized to be truly useful, however, or else customers end up with “Sorry, I don’t understand that” responses.
There’s another significant opportunity for AI that takes an adaptive approach to the insurance-buying experience: tailoring the tone and purchasing journey based on specific customer profiles and inputs, which would ultimately remove the need for irrelevant questions and steps.
As more data and experiences come in, machine learning technologies can iterate over permutations to find subtle patterns and relationships between data points that are only apparent after it acquires a greater population of applicants. It can go beyond human analysis to uncover intricacies that most people would miss.
This machine-based process gives life insurance customers added layers of value for the information they hand over during the application process. For example, it enables providing an instant decision on coverage and offering more competitive pricing due to higher accuracy and therefore less risk.
There are limitations, for now, with machine-based underwriting. The machine learning is used where it can come to a confident decision based on the data inputs and underwriting rules it receives. For more complex cases, or until it has learned from enough scenarios, the machine is programmed to know when the analysis should be handed off to a human for a more thorough review.
