In it’s bare essence, insurance is simply an agreement in which one party agrees to take on a risk of another party, for a fee. However, the very conflict of interests that arises from this simple transaction, turned this industry over the years into a convoluted myriad of entities with competing interests. In this post, I will try to unpack the relationships between the different entities, in order to create a framework that will help predict the disruptive powers of AI technologies in this space.
I am a huge fan of Clayton Christensen’s literature on innovation. Ever since his 1997 book, The Innovator’s Dilemma, and up until the day he passed away earlier this year, he was widely considered the leading expert in what makes an innovation disruptive. In fact, he was the one who coined the term “Disruptive Innovation”, which represents an invention which incumbent vendors are neither able nor willing to compete with (as opposed to a “Sustaining Innovation”). His ideas were so groundbreaking, because he presented a first-of-its-kind theory that was finally able to explain why some innovations are successfully introduced by small startups competing with the behemoths of their industries, contrary to all other logic. If I tried to distill his whole theory into one sentence it would be this one: an emerging technology is more likely to be disruptive if it does a job that’s already being done by consumers, but while the other solutions are good enough for this job, they are either over-serving some customers (i.e. they don’t need all the features) or are just too expensive for other potential customers (i.e. there’s an under-served market), but the resources, processes, and values of the incumbent vendors make the new technology distasteful for them. I know it’s a mouthful, but it explains why exploring the insurance value chain, its jobs-to-be-done, its resources and its processes may help us make predictions for emerging technologies.
There are four discrete parts to the insurance value chain - distribution, underwriting, service, and claims processing. Similar to any other product, distributors are in charge of finding a prospect customer and convincing them to purchase an insurance policy. However, because of the complex nature of the insurance product, and the way insurers compete on both the price and the terms, brokers have emerged as distributors that represent the customers’ interests. While brokers typically get paid by the insurer, they are essentially advisors to the end customer, and are not tied to any single insurer. Agents, on the other hand, only distribute products of a single insurer, whose sole interests they represent. Lastly, some insurers have a direct distribution channel, where they sell directly to the end customer.
Underwriting is at the very core of what an insurance policy represents - it is the act of agreeing to take on a financial risk. Within the insurance industry, it can be further broken down into two separate processes - the first process happens before anyone purchases a single policy, and in it actuaries create a mathematical schema for how different factors should affect the price and the insurer’s willingness to accept the risk. The second process happens every time a prospect applies for an insurance policy, and in it the underwriting operations professionals collect the different data points required by that schema to decide whether to write the policy, and at what premium. Different insurance products require different underwriting data, and can take anywhere between a few minutes to a few days. The first process of creating the pricing schema is always done by the insurance carriers themselves. The second part of using the schema to make a final decision for a specific prospect client is also usually done by the carriers, but can also be outsourced to super-brokers whom the carrier trusts. These brokers are called MGAs (managing general agents). A different kind of underwriting happens when some insurance carriers subsequently offload some of their risk portfolio to reinsurers. The latters, will underwrite some of the risk in return for some of the premiums, but they do not sell individual policies to individual customers.
In a typical insurance policy life cycle, there are two main points of contact between the insured and the insurer - purchasing the policy, and filing a claim. Any interaction that happens in between, like changing beneficiaries or making payments, falls under the category of service. For policies sold through agents and brokers, much of the servicing of the policy would be done via the broker, who will serve as an intermediary between the insurance carrier and the insured. With that said, insurance carriers, even ones who sell strictly via agents or brokers, all have customer service call centers servicing both these intermediaries and the end-customers.
The final part of the insurance value chain is the claims processing. Processing a claim involves accepting the first notice of loss, then determining fault and the insurer’s liability. Different types of insurance products have completely different processes for claims. Most health insurance claims, for instance, are processed electronically and immediately between the medical provider and the insurer. In property claims, each claim will be processed by a dedicated claims adjuster, who will appraise the damages, will determine how much of the damage was caused by the covered event, and will set the appropriate compensation. For some insurance products carriers may outsource the claims processing to third party administrators (TPAs).
The insurance value chain
In the very first paragraph I have mentioned a conflict of interests that arises from an insurance transaction between the insurer and the insured. As a matter of fact, insurance demonstrates a special type of conflict of interests, which economists call The Agent-Principal Problem, in not one but multiple parts of the insurance transaction. The agent in this problem is someone who can take action (the insurer) on behalf of the principal (the insured), and the problem arises when asymmetric information between the two leads to so-called “moral hazards”. That is, one side of the transaction holds information that the other side does not, which motivates them to mislead or misinform the latter. For example, the complexity and overlapping nature of some types of insurance products mean that the distributors understand better than their clients which insurance policies best suit their needs. The moral hazard in this case is that the same distributors may sell products based on their profit margins rather than the real needs of their customer. In some cases, this even leads to customers purchasing coverages that they already have from other policies they own. On the other hand, when it comes to claims, the insured holds all the information about what really happened. The moral hazard here is the motivation to embellish the damages in order to get over-compensated.
Over the years, several models have been developed in attempt to reduce the severity of the different agent-principle problems in insurance, by either reducing the information gap or by increasing the kinship between the insurer and the insured. Mutual insurance is probably the oldest model, in which the policyholders themselves own the insurance company. This increased kinship between the insurance company and the insured reduces the motivation to claim for damages fraudulently. The smaller the group of policyholders and the closer their relationship is, the more effective this technique in eliminating the moral hazard, because claimants would not want to withhold future profits from other policyholders they personally know. Lemonade Insurance, is an example of a new-age insurance company trying to overcome the size and relationship limitations of the mutual insurance model. To do so, they require each policyholder to select a charitable organization, as part of their onboarding process. Lemonade then portrays their business model as similar to any other service - they only charge a flat 20% service fee out of the premium. Any unclaimed premiums are then distributed to the selected charity. Lemonade's assumption is that if I am part of the group who donates to the NAACP, for example, I wouldn’t want to be responsible for withholding donations I already designated for them. Hence, I would not try to add undue damages to my claims.
As opposed to home or car insurance, life insurance is complex to a degree that requires some expertise to purchase. If you are rich enough, you may pay a financial advisor to compile your portfolio of investment and risk products. The average Joe, however, is left with the moral-hazard-laden brokers and agents. The age-old insurance industry idiom represents this problem well: “life insurance is sold, not bought”. This is not to say that anyone trying to sell you life insurance is morally skewed, only that they are incentivized in a way that increases their propensity to such. I am not aware of a publicly available perfect solution to this flavor of the agent-principal problem, but insurance brokers do play an important role in minimizing its effects for the insurers.
In recent years, AI has been a buzzword that was tossed around as a technology that would disrupt many industries, including insurance. It is important to understand, however, that AI is not one single technology, but a whole family of research areas that focus on computer systems that mimic human-like traits, like learning and reasoning. With that being said, as the technology progresses, AI is becoming more and more synonymous with Machine Learning in particular, which in itself is a huge research field within computer science. Machine learning encompasses many different types of algorithms. Their common denominator is that they all typically rely on training data to perform a predefined task, by trying to minimize a number calculated by a carefully crafted “loss function”. Different loss functions are made for different tasks - anything between recognizing handwriting and creating deep-fake videos of Donald Trump. Now that we’ve established the main links in the insurance value chain, as well as the resources and processes they use to do their jobs, we can go ahead and evaluate the potential of AI to attack the over-served or under-served parts of this value chain.
Within the distribution part of the value chain, we can now try and evaluate the applicability of AI to the different jobs - finding prospects, convincing them to purchase a policy, and reducing the severity of the agent-principal problem. Because AI relies heavily on data, the more consumer data a company has, the better it is equipped to use AI algorithms. The data hoarders of these days - like Facebook, Google, and Amazon, are therefore becoming superpowers of using AI to match consumers with the services they need. Even if traditional insurance distributors wanted to use AI to find new customers, in most cases they don’t have the data to support it. In fact, while their resources and processes aren’t geared for collecting this type of data, they are actually happy paying for hot leads and already have the processes in place to share revenues with 3rd party distributors. One type of data that they do have is recorded conversations. Conversational AI can use this data to match the best sales pitch for every type of prospect. This is where traditional insurance distributors (brokers, agents, carriers, etc.) can collaborate with conversational AI experts to create an optimized AI-driven insurance sales (and service) robot. What about the agent-principal problem? A data-hoarder that has a pristine reputation, may use their strengths to become a super-broker for all carriers, as long as they don’t underwrite the risk. Perhaps selling insurance will not contradict “do no evil”.
Using conversational AI to service insurance agents
Actuaries are one of the insurance industry’s strongest resources when it comes to AI. Regression analysis, which is one of the tools of the trade of actuaries, is actually considered the most basic example of machine learning. Insurers also own tremendous amounts of historical claims data, which means that for anything on the underwriting-claim axis, AI techniques are actually the epitome of sustaining innovations. They can increase the accuracy and speed of the yes / no / how much decisions in these jobs, which we can safely say are not done “good enough” yet. Taking car insurance as an example, one can easily imagine a future world where the DMV gives the carrier the information it needs about the car and the driver (if drivers are still a thing in this future) in order to generate an instant quote. When a crash occurs, the car’s sensors will relay the information directly to the carrier who will immediately reimburse for any casualties or property damages.
As we’ve seen, AI will definitely help improve many of the jobs done in the insurance industry. The key to understanding who is going to capitalize on these advances lies in the data ownership, and the expertise required to use it. It is easy to imagine a day where all this technology will turn insurance into a simple service, that’s almost transparent to the consumers. It may be purchased automatically, underwritten instantaneously, and cover losses independantly.
