Insurance in the Age of AI, Block Chain and an Overabundance of Data

The traditional insurance model has had a pretty good run. It has been slowly evolving over the past few hundred years to include new coverages, multiple distribution channels (broker, agent, online), and create more complex actuarial models. The financial industry has been known to be relatively slow adopters of new technology, mostly because companies simply cannot take undue risk — and those working in insurance are experts in minimizing risk.

This article is going to outline the current state of insurance, and the current progress technology has made, and will argue that the industry may be on the verge of significant disruption — the likes of which has the potential to render most policies, companies, employees and value added services obsolete.

Two things to keep in mind as we move through this piece.

  1. Nobody likes insurance. They need insurance, therefore they don’t like it. It’s a necessary evil, and the perceived value isn’t there for those who do not have claims and have limited risk
  2. Technology has increased the rate of change to the point where the time it takes science fiction to become a reality can be reduced to months. The telegraph was the only mode of long distance communication for over 50 years before the telephone was invented. And even then, it took an additional 60 years before they were mainstream. Twitter started off with 400,000 per quarter and grew to 100,000,000 per quarter after one year. Now, there are 6,000 tweets per second and 200 billion (with a b) per year.

Artificial Intelligence

There are a number of terms used when describing AI. Neural networks, machine learning, deep learning, the list goes on. This article will not provide a rich overview of how artificial intelligence works, there are a number of great resources that do that already. But let me give you the Cole’s Notes version. Traditional computer programs and algorithms have been exceptionally good at performing computational tasks. Give it a problem to solve, or some code to run, and it can do so significantly faster than a human ever could. The bottleneck with that approach is that it needed a human to give it clear and specific instructions on exactly what to do. Furthermore, the computer could only process data that it could understand, which has typically been something coded into a language a computer can translate (programming language/machine code) or broken down into pure numbers (computers are great at math). What traditional computers haven’t been able to do, in clear contrast to AI, is come to it’s own conclusions and uncover for itself the best solution given a specific rule set. Furthermore, computers are getting really good at understanding non-traditional sources of information. Image recognition, speech recognition, unsorted datasets. When given access to a large number of YouTube videos, DeepMind’s AI taught itself what a cat looked like, and became very good at identifying cats against other images of animals. Nobody told it what a cat looked like, it learned that on it’s own.


Everyone’s favorite subject. It’s the technology that is going to change the world, and none of us really know what it is. Again, I’m just going to provide a nutshell description of the technology, and it should become clearer as we go through the thought experiment below. Blockchain technology provides a single ledger of transactions that is secured by cryptography and the fact that everyone works off the same books, at the same time. You cannot change the record maliciously because your update will be in conflict with what everyone else’s records hold as the truth, and therefore it will spot the lie. Blockchain effectively reduces the need for traditional institutions because it increases the self service capability without the risk of fraud. It means that the gatekeeper mentality is no longer necessary because once the rules are established, everyone gets forced into playing nicely together, tracking whatever transactions are implemented.

The real cause of disruption for insurance — data

We’ve heard the stories about AI for awhile, but it’s nothing really new for the insurance world. Analytics has been a growing part of the industry for years, and it’s obvious that with increased computing power we would inevitably evolve the rating algorithms and risk assessment tools to use the latest available tech. The problem is that the AI boom is only made possible by the explosion of available data. AI needs huge datasets to learn from, and these datasets are collected and aggregated by some of the largest companies in the world. Google and Amazon are fighting toe to toe in a race to develop the smartest most capable AI, and others are joining that fight constantly.

Let’s step through the traditional method of getting insurance. First it starts with someone seeking out insurance, physically deciding that they want/need, and then shopping for it. Then they go through the drudgery of providing all of their information, which is tedious because they don’t readily know 90% of it, and because they also know that everything they say from here on in will impact the price of their insurance. It’s kind of like going to a new doctor every time without any patient records, and relying on the patient to provide their entire medical history from memory — guaranteed they will some things wrong, or leave some things out. And the more complicated their medical history is, the higher the likelihood of them forgetting something, and the greater chance that whatever they forgot was very important.

Now let’s contrast this to Google. A quick look through my Google Account and Privacy Settings shows a detailed history of everything that Google knows about me. They have my banking institution, my spending habits, obviously my address, where I work, how I get to work, what music I’m into, what food I eat, my hobbies and interests. I also have a Nest thermostat and smoke alarm, so they have real time monitoring of my house’s temperature. But I also upload all of my photos to Google Photos. So they have pictures of the inside and outside of my house. They also have instant access to my photos if, let’s say, I get into a car accident and my first response is to snap pictures for proof. Your first response might be “you should do more to protect your privacy” and my answer is that I already do. I am relatively conservative in my approach to letting “Big Brother” track me. I’m an informed user with a tech background and I do monthly audits of my privacy settings and sift through my personal data to weed out anything sensitive. My question to you is “when is the last time you reviewed your privacy settings on Google, Facebook, Twitter, Instagram…?”.

Customer acquisition over profits

In the data driven age, profits are not your first priority, what you’re looking for is customers and data. If you acquire enough users, you will be able to collect enough data, and many companies rely on that data to bring in the money. Companies will either sell that data, use the data to teach their algorithms, or analyze the data to determine how to become profitable. More data equals higher levels of certainty, and you move from guessing to knowing when making decisions. It took Amazon 14 years to become profitable. How many companies sat idly by saying “Amazon’s business model isn’t sustainable, they aren’t even profitable yet”? Tesla still isn’t profitable, having started manufacturing electric cars in 2003 and being valued at $51 billion. Just because a company isn’t profitable today does not mean it won’t become a world leader tomorrow.

The Future

This is the story of a fictitious insurance company named Acme Insurance. The board of directors at Acme decided that the most important thing for their company to do is acquire as much data as possible and start using AI to rate policies, check claims for fraud, and to determine the best risks by segmenting the market into micro markets and focusing all their efforts on acquiring the right risks.

Their approach was simple, direct sales to consumers, 100% online, a beautiful user interface that was intuitive and easy to use. They undercut the market by minimizing their expenses. Investment was small because they ran on Amazon Web Services, therefore only using the computing power they needed.

To identify their initial target markets, they hired contract programmers who had experience scraping the web. They scoured the internet for every quick quote tool, price comparison website, rate manual, info sheets etc. that were publicly available. They then ran scenarios against these data sources to determine which risks received the best rates. This let them use the knowledge and experience of the established industry to determine how to rate their clients. They also used the loss ratios and experience from each insurance company to weight their algorithms, so Acme’s rating would more closely reflect higher performing companies.

Now that they knew how the industry priced risk, they hired the top digital marketing firms to go after the most attractive clients. Marketing online is significantly cheaper and easier than traditional marketing. They mapped out their demographics based on what social media platforms cater to each group. They used information from Google search to figure out where these people lived, and what they searched for online. They used sentiment analysis AI to read and understand all the reviews found online for their competitors, from Google to Glassdoor to Facebook. Using all of this information, they determined the pain points for their key target markets, exactly what language they use to describe their frustrations, and exactly where these people spend their time online.

Acme is growing at an astronomical rate, increasing it’s book by about 40% month over month. About 6 months in, they start to see the claims that go with that growth. Luckily enough, the company is growing so fast that it artificially keeps their loss ratio to a controllable 200%. The company is losing money, but with every claim they are gaining equity. The way they are doing that is by scrutinizing every claim to teach an AI to better predict fraudulent claims and better understand risk factors. Every minute detail of a claim is tracked and analyzed. The system collects all like policies, determines the exposure of risks with similar geography, coverage, customer demographic, construction type, build year… The AI then incrementally adjusts the rates, limits, deductibles and wordings of those coverages and details to fine tune it’s algorithms to cover future losses. But it doesn’t just have one set of rates and rules. The system maintains 10+ different rates and rules in order to test them against their policies to determine which action made sense. Some policies will see an increase in premium, some will see a drop in limits, some will have increased deductibles, some will have all 3, some will see no change. By doing this, Acme can perform an A/B test on it’s book to determine which actions properly mitigate risk, but also which actions are acceptable by their user base (by tracking number of cancellations).

After 5 years of rapid growth and losing money, Acme insurance seems to be a failure. They grew too big too fast. They tried breaking into too many different markets, servicing different countries, and stretching their region too thin. They weren’t advertising using traditional methods, so most people hadn’t even heard of them before. As a private company that only employs roughly 100 staff, they didn’t seem to pose a threat. Furthermore, their CEO had been active on social media, and was quite transparent about the unsustainable losses they experienced. With over $400 million in venture funding, everyone from big banks to housing developers, even governments had jumped on the bandwagon. The company appeared to be a bust. But that was from the outside looking in.

Year 5 marked a turning point for Acme. Their client base reached 50 million users world wide, writing in just about every developed country. Their learning algorithm used it’s ability to constantly retrain and learn with every new policy, claim and customer complaint to successfully rate any building or automobile, anywhere in the world. They were also able to successfully eliminate the individual from the rating equation, making decisions purely based on the physical item that was insured. By partnering with a number of companies developing smart home technologies, the vast majority of their houses were outfitted with appliances and sensors to speak with their AI and the insured in real time. This provided constant reminders to minimize the risk of a claim — a hot spot reader will alert the insured that a candle is still on when they head for bed, the scheduler will notify the insured that it’s been 2 years since they cleaned their wood fireplace, and the smart stove automatically shuts off if it detects too much smoke in the range hood. Acme also had partnerships with every major automobile manufacturer, and created a data sharing pool which allowed them to gain insights into driving patterns, and in return Acme was able to significantly discount insurance rates on new cars — effectively reducing the cost of ownership and acting as a discount. All policies were rating month to month, and were based on the previous month’s usage and learnings.

Acme’s final move was to leverage governments to allow it’s product to be sold in conjunction with mortgages, car loans, property taxes, and in some cases they just rolled the predicted cost into the purchase price. Acme’s algorithm was so successful that it could confidently assess the 5–10 year premium for certain makes and models, and since most people own new cars for roughly 10 years, they simple added it onto the initial sale of the car. Acme also began leveraging it’s capital to hire more and more climate scientists, and began modelling weather patterns in-house. The company became synonymous with environmentalism and safety. Acme rebuilt damaged homes using state of the art building materials to prevent future risks, such as breathable concrete, fire suppression systems, and hot spot detection devices. Even houses located in flood plains received giant inflatable balloons that would circle the entire house to protect it from floods.

History would look fondly on the impact that Acme had, not just as a business and AI developer, but because it saved lives and changed the world. Acme’s largest financial risk was climate change, and so the company leveraged it’s relationships with governments and it’s positive relationship with it’s insured to make drastic changes towards sustainability. Acme led the charge towards fighting climate change, developing self driving cars that reduced traffic accidents to zero, and eliminated the need to shop for the best price for insurance.




Vice President of Business Innovation and author of Futurist, tech enthusiast, entrepreneur. Currently researching artificial intelligence.

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Stephen Matusiak

Stephen Matusiak

Vice President of Business Innovation and author of Futurist, tech enthusiast, entrepreneur. Currently researching artificial intelligence.

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