Intro
As data and technology become more accessible, insurance companies are using data to outperform their competition with three main strategies: 1) introducing new business models 2) capturing new value and 3) leveraging new cost structures.
New business models are here to stay
The direct-to-consumer (D2C) model is well known and relied upon heavily across many industries including auto insurance. A number of factors have prevented commercial insurance carriers from selecting the D2C model though. In fact, direct response carriers write less than 1% of all commercial and workers’ compensation premiums according to the most recent Market Share Report by the Independent Insurance Agents and Brokers of America (IIABA). According to the IIABA, 20% of auto insurance is sold through direct response carriers, a far greater proportion than commercial carriers.
One of the factors that puts auto insurance at an advantage over commercial insurance is the simplicity of the product; hence, a shorter submission process and shorter submission form. That type of experience is what customers expect when buying a product online. Up until now, this reality has stymied commercial insurers. However, new data sources are enabling commercial insurers to simplify insurance applications through submission form pre-fill or complete digital on-boarding.
Next Insurance, an MGA turned insurance company, is offering insurance online and drastically reducing the number of questions in a questionnaire using new data sources and leveraging artificial intelligence and machine learning technology. In fact,most Next Insurance customers can buy a policy in 5 to 10 minutes. There are a few other important factors that are stopping commercial insurance companies from a D2C model, yet with data from third-party sources, they do not have to worry about the poor customer experience of a long questionnaire.
One of the ways insurance companies are using their own internal data is to establish new adjacent businesses. Insurance companies do not need to collect new data, rather they can utilize and repurpose the vast amount of data they are already collecting . An insurer can sell the data anonymously to other industries that find this data interesting. This could be useful for predicting trends of a particular car company to even predicting the direction of the entire economy. Quandl, for example, gathers daily counts of how many new car insurance policies are sold by auto insurance providers and sells this information to investors. An adjacent business revenue stream like this is not tied up for liquidity compliance, bringing even more value to insurance companies.
The Internet of Things has enabled the pay-per-usage business model to be introduced to the car insurance industry. MetroMile is a new car insurance company that calculates insured premiums per mile. MetroMile uses sensors to continuously track mileage, speed, location and more. The data collected enables MetroMile track the costs the customers need to pay.
Capturing of new value
Lloyd’s Emerging Risk Report about drones highlights negligent or reckless pilots as one of the fundamental risks of drones.
Many insurers ask if photographers use drones but they do not ask more details about that usage. This can be troubling for insurers, as different uses have different consequences if the pilot is negligent or has an accident. A wedding photographer who has an accident with a drone, for example, would face very different consequences than a landscape photographer would. Not knowing this information during customer acquisition or being too narrow with eligibility requirements can hurt an insurer.
With up-to-date data, carriers can develop new business streams in various directions. For example, as a business changes over time and expands to new areas by offering new services or getting exposed to additional risks, carriers are able to automatically offer additional insurance coverage.
Furthermore, carriers can quote businesses based on their exact risk rather than that of other businesses in a similar category.
Leveraging new cost structures
Data is enabling new and veteran commercial insurers to minimize costs and leverage new cost structures. Lemonade insurance is maximizing automation to create a cost structure focused on minimizing the company’s employee costs by being lean. Being lean is such an important part of Lemonade’s cost structure that it recently wrote in ablog post that it has 2,500 policies per employee compared to other “efficient legacy insurance carriers that have about 1,200 policies per employee.”
Lemonade’s digital platform takes in data through third parties and its own customers at the point of policy issuance and stores it for easy use across the insurance value chain. Thus, data automation at Lemonade drastically reduces the need for full-time employees to process and handle new and current insurance policies, as the data already moves policies through the customer journey automatically.
Lemonade also handles claims through data. Lemonade’s claims bot can review a claim within a few seconds. The chatbot cross-references the claim with the policy data and applies anti-fraud algorithms, reducing the need to have a person review every claim that comes in. Furthermore, Lemonade automatically collects all incoming data, increasing the accuracy and speed of the anti-fraud algorithms and ultimately reducing false claims.
Summary
Since the inception of insurance, insurers have relied on data and statistics to determine the cost of risk and to set prices for their insurance policies. The above examples are just the tip of the iceberg of how insurance companies are using data to not only survive, but thrive.
Data improves the customer experience through bots, simpler insurance applications and more tailored insurance. Insurance companies also have the opportunity to use their own data to create new businesses that can be separate from insurance entirely. With data continuously becoming better and more available, it will be interesting to see how insurance companies continue to use data to grow.
