How Algorithms Can Make Life Insurance Offers Fast

Due to advances in AI, properly built algorithms using approved data sources can produce accurate life insurance quotes in only five minutes.

Happy woman sitting at table with laptop

Today’s consumers expect fast, efficient buying experiences for most products and services. According to Forbes, two out of every three respondents to a recent survey said speed is just as important to them as an item’s price, and over half said they would hire the first business that responded to them, regardless of whether its product or service was more expensive than alternatives. Similarly, about half reported being less likely to spend money at a business that takes longer to get back to them than they expect.

Customers for life insurance are no different. The good news, however, is that due to advances in AI technology, properly built algorithms using approved data sources can produce fast and accurate life insurance quotes. For instance, our online tool underwrites and prices insurance applications in only five minutes.

By speeding up the underwriting process, life insurance providers can convert prospects into policyholders. Whether working through agents or directly with consumers, producing an offer quickly, efficiently, fairly and accurately is essential both to the growth of the insurance business and to help cover the uninsured.

See also: Insurance Underwriting Will Never Be the Same

The problem of lengthy vetting processes

Traditionally, life insurance companies have required prospective customers to endure a lengthy vetting process. For instance, many consumers had to endure multiple interviews with carriers’ representatives and get an exam with a blood draw or urine specimen.

In recent years, the industry has evolved, and many companies have started offering accelerated underwriting solutions. In my experience, however, the many restrictions put on applicants mean that only a select few qualify for these programs. Most are still required to answer lengthy questionnaires.

Imagine answering a question about your medical history only to find yourself confronted by five to 15 (or more) additional questions about that condition alone. Now imagine you are confused about how to respond to some of those questions. For others, you don’t even know the answer.

It would make sense for you to feel frustrated and annoyed, yet this is the life insurance industry’s usual approach. Companies design their onboarding surveys with reflexive questions that bog people down instead of ushering them through the sales funnel.

Expecting prospects to tolerate such a negative customer experience leads to decreased conversion rates because many people will elect to abandon the process altogether. Meanwhile, those applications that achieve completion tend to be less reliable because the intensive questioning encourages the consumer not to disclose further medical conditions. If you feared triggering a seemingly endless series of still more new questions, wouldn’t you think twice, too?

How next-generation life insurance algorithms work

The science of polling and survey research allows for a fast, non-reflexive method of interviewing people such as we use in QUITM. This approach gets the key information it needs to make accurate decisions without needing to ask follow-up questions. This cuts down on the amount of time the disclosure process takes.

Essentially, when a consumer discloses a condition, a multiple-choice question is presented that is tailored to that specific issue. The consumer will choose the answer that best describes the severity of their condition.

The system uses the consumers’ answers to derive an instant decision in the form of a provisional quote. Algorithms can rate and price consumers in a matter of minutes, offering a product that fits a budget and risk profile. Providing the most accurate quote as quickly as possible is essential to limit surprises when the final offer is presented to the consumer. This ensures a positive customer experience.

If the customer chooses to move forward after receiving their instant offer, they consent to order third-party data, which includes verifying their identity, conducting a criminal background check and obtaining their driving record and their prescription history. To produce offers that avoid bias, AI-based algorithms should only use standard, industry-accepted data sources and purposefully exclude information from social media or data that would base decisions on the consumers’ race, religion, employment, location or buying habits.

Layering on this industry-standard third-party data is essential to mitigate insurance providers’ risk. It serves as a check against the consumer disclosure because the accuracy of the provisional quote depends on how honest the consumer was when disclosing their medical history.

Algorithms can analyze this data and quantify its degree of confidence in the provisional quote. If the confidence level is high, the system can make a final offer. If it’s lower, the system can order additional data, such as medical claims and digital health records

In our experience, approximately 80% of the time the final offer will be the same as the instant quote.

See also: The Underwriter 2.0, in the Era of AI

Pleasing customers boosts business

According to Zendesk, “73% of customers now say CX [customer experience] is the number one thing they consider when deciding whether to purchase from a company.”

In my experience, consumer drop-off increases as they are asked more and more questions and forced to spend more time answering them. That’s why we designed a life insurance platform that delivers the consumer’s desired outcome in a minimally invasive, five-minute experience. 

By eliminating reflexive questions, assessing and pricing every application and delivering instant offers, life insurers can develop a customer-friendly buying experience while reducing abandonment rates.

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