The process through which customers and insurers find each other and bind coverage is called “matching.”
The frictions of this process represent one of the highest costs of the traditional insurance model, as insurers acquire customers via expensive advertising campaigns or other marketing expenditures and lengthy information exchange required for matching. These customer acquisition costs are priced into the premium to allow the insurer to break even in expectation. As a result, many customers prefer to self-insure, and these will tend to be the least risky customers from the insurer’s point of view.
However, current technological capabilities can significantly reduce the friction of this matching process, and embedded insurance is a technology developed specifically to achieve this ideal of frictionless matching. As a result, all customers can be profitably matched with a willing insurer, and information is painlessly exchanged at the point of purchase.
With customer acquisition costs (CACs) at effectively zero, premiums should reflect only expected claims expenses, taxes, and administration costs, so that almost all customers will be willing to acquire insurance. This process maximizes the market size and minimizes risk.
By removing CACs, frictionless matching positively impacts both the supply and demand curves for insurance.
Frictionless matching reduces the CACs by capturing the top of the insurance funnel: it pairs the acquisition of the insurance policy with the acquisition of the good or service to be insured. The insurer doesn’t have to search for customers – the customer pool of the insurer is the same as the customer pool of the provider of the good or service. Since the embedding model captures the customer group at the top of the funnel, it could overcome adverse selection.
By seamlessly reaching various customers at the point of purchase, we estimate embedding could enable a 78% reduction in CAC. The decrease in cost shifts down the average total cost curve and increases supply. This shift allows the insurer to provide coverage at a lower premium and capture a more significant population segment. Now, all types of customers can be insured at the incremental, lower-risk customer valuation.
Simply put, embedding insurance adds a group of lower-risk customers, reduces adverse selection, and lowers underwriting risk. It’s a win-win for everyone, including the high-risk customers who traditionally had to bear the burden of ‘switch-and-save’ schemes.
As mentioned earlier, embedding can provide customers with greater convenience and timeliness: they don’t need to search for insurance policies themselves. By eliminating search costs, embedding drives up customers’ valuation and shifts the demand curve up for insurance. Consequently, customers who previously had very low valuations are now willing to buy insurance.
The effect of embedding on the demand curve further increases the surplus internalized by both the insurer and the high-risk customer. As a result, the shift of the demand curve further reduces the average cost of providing insurance. The inclusion of even more low-risk customers to the insurer’s total pool further decreases the premium paid by the high-risk customers.
The combined effects of frictionless matching on supply and demand lead to an expansion of the market and a reduction in risk and average claims costs.
Contributing Authors from Oxford University:
Jacqueline Dai, Laura Fritsch, James Hall, and Mungo Wilson