The human bias

At a time when Canada’s property and casualty insurance industry is exploring how to use artificial intelligence and analytics to reshape the insurance customer experience, it is important to remember the role of the human decision maker.

Unlike Data — the android in the television series Star Trek, whose positronic brain allows him to make sophisticated computations — humans rarely make decisions with actuarial precision. Instead, our decision-making process and purchasing behaviors are shaped by psychological, emotional, cognitive and social biases. We must recognize and acknowledge these biases if we want to fully leverage AI and analytics to improve how customers purchase and experience insurance.

Pain of Buying

According to certain economic principles, businesses should set prices at the point at which the demand for a product or service is equal to its supply. At this equilibrium price, both consumers and producers are satisfied.
In reality, consumers’ purchasing behaviors rarely work this way. We recently studied the behaviour of people who were looking for credit protection insurance for mortgages. Our study showed that price sensitivities increased dramatically when the monthly insurance premium exceeded the underlying monthly mortgage by 2% or more.

Instead of considering the absolute price of the insurance premium on its own, customers used their monthly mortgage payment from which to benchmark the value of the insurance coverage.

Another cognitive bias to take into consideration is the way we experience pain during the purchasing process. In a 2007 study with 26 adults, researchers at Carnegie Mellon, Stanford and MIT found the pain centres of the brain would activate when subjects saw prices they perceived as being too high.

Pricing sensitivities and the psychological pain of buying can be compounded in the digital age: a customer presented with product information that overwhelms, obfuscates or appears overpriced will simply move on to a competitor’s offering a few clicks away.

To improve the buying experience, insurance professionals must do a better job of conveying to a prospective customer the product’s value and its relationship to the price. Let’s consider the example of credit protection insurance. In a recent project, we presented customers with four types of credit protection insurance in the event of death, disability or job loss. We found that customers heavily favoured buying life insurance over disability, critical illness and job loss coverage. Also, they did not understand the benefits of the various coverage options.

Instead of presenting price in isolation, which only serves to convey a loss (and invites comparisons with cheaper prices), we provided shoppers with reference points to support the relationship between price and value. We presented a visual product line-up, re-ordering them into a descending sequence. The highest value was presented first, before those with lower values, in a quantifiable form. The link between value and price was then made obvious to the human shopper.

To improve the insurance purchasing experience, we can leverage predictive analytics to provide guided recommendations for different customer types. We can also build an analytical understanding of our customer’s pricing sensitivities, and tailor pricing levels and product recommendations accordingly.

The Desire for Fairness, Equitable Outcomes

Another psychological bias we must consider is our innate social preference for fairness or equitable outcomes. This issue hits a raw nerve with many insurance customers, a fact on which insurance startup Lemonade has been quick to capitalize. On its website, Lemonade says “traditional insurance companies make money by keeping the money they don’t pay out in claims.” Lemonade goes on to explain: “this is why getting your claims paid fast and in full is sometimes so hard.”

If insurers want to maintain the trust of their customers, they must improve the perception that the industry has a deliberately slow and cumbersome claims process. On this front, there is an opportunity for insurance companies to leverage AI and behavioural economics.

In a recent call to Citi about an overdraft fee, I was pleasantly surprised when the automated customer service algorithm not only narrowed down why I was calling, but also automatically waived the overdraft fee. Citi’s AI-powered customer service was able to quickly recognize my relationship with the bank, and resolve my issue in under five minutes. In the same way Citi leveraged AI to address a common pain point of its most valued customers (overdraft fees), insurers can do the same by transforming the long, complex claim process into a more streamlined and efficient one.

We must invest the resources to understand the emotional, cognitive and psychological factors that make the insurance experience a challenging one for our customers. Emerging technologies like AI present a unique opportunity to address our customer’s pain points and reshape a customer experience worthy of the digital age.

Wei Ke, Ph.D. is a managing partner at consulting firm Simon-Kucher & Partners.