Anyone who has spent any time at an insurance company has heard the saying “there are no bad customers, just bad pricing.” Seemingly innocuous on the surface, it is symptomatic of the fact that the customer and the insurance company aren’t in a mutual partnership. Often, it's true. Traditional insurance companies rarely think about individual customers and businesses, instead choosing to focus on collecting data that transforms the company into a statistic. Corvus turns this approach on its head. We use data that measure the underlying causes of losses, and we make our clients an active participant by mining and sharing this data to help take corrective action.
Breaking the Traditional Insurance Model
The traditional insurance pricing model focuses on aligning the cost of insurance with expected future losses, using data collected from a wide variety of sources to do so. In a market with many competitors, there is constant pressure for companies to charge more for risky customers, or else have their low-risk customers leave for a lower-priced insurer. In the extreme, aligning price with a customer’s risk breaks down the social goal of insurance in the first place: to pool society's risk and help each other overcome fortuitous events. We see this in many cases through government-backed insurance programs where private insurance companies are unwilling to participate for the riskiest customers.
The issue with this approach is that while insurers collect a lot of data from many sources, they’ve historically been limited in the depth of that data. As a result, most business insurers attempt to correlate abstract variables about a business with insurance claims. In the case of cargo insurance, they may use information about the age of the truck, the type and annual amount of goods being shipped, and even business credit score. While these components correlate with future expected claims, from the perspective of improving the client’s business and reducing risk, they are meaningless. Traditional insurance companies rarely share this data with customers in any meaningful way, and even if they did you’d be hard-pressed to find a use. No company in their right mind is going to ship fewer goods to save money on their insurance.
The Corvus Approach to Data-Based Smart Insurance
Corvus is taking a new approach to insurance, capitalizing on the proliferation of data at more granular levels and without legacy systems and practices holding us back. We seek out insurance products that have a large amount of untapped data, but most importantly, untapped data which measures the underlying causes of the insurance risk— as opposed to generic information about the client’s business. With our Smart Cargo Insurance product, we use the temperature data from an individual’s shipments to identify when their cargo is most susceptible to spoilage. With our Smart Cyber Insurance, we can identify a customer’s IT security vulnerabilities that enable outside parties to breach their data. By moving our data closer to the true source of risk, we’ve created pricing and underwriting unique to each business’ risks, which makes for increased transparency and fairness for all customers.
How Corvus Utilizes Data to Improve Products & Services
Having all this data is great, but if we only use it to charge our customers different prices, we’d be no different than every other company. Instead, we take transparency a step further by sharing this novel data in our Dynamic Loss Prevention report, providing insight on the factors in a client’s control that are most likely to prevent claims. Where traditional insurance companies penalize risky companies by charging them more money, Corvus aims for a mutually beneficial approach that reduces losses, putting both parties in a better situation. We are living in a data-driven world, and it’s time for the insurance industry to step up to the bat and play. At Corvus, we are using this data to center clients' needs and to mitigate their risk— and we’re doing it transparently, so customers can make the choices that are best for them.