Getting the Price Right

The world is increasingly data driven. Since the advent of the internet, we’ve seen huge amounts of data flow into the world. One of the things that we still see is that the technology and the tools that have been used to insure and underwrite risk really take a very static snapshot of data at a given time. It’s typically a fixed digest of information representing a history of losses and some of the data about the risks. If you think about the amount of data in the world right now, it could be much more dynamic. If you were insuring a fleet of airplanes, you might think about the history of losses they’ve had and the locations that they flew to and from, and that’s all you had to analyze the risk. In today’s world, you can go online, and you can see the flight path of every plane flying in the sky. That’s dynamic information, that’s real time, it’s up to date, and it’s a much richer picture of the exposure and the propensity for loss than we’ve ever had before. Yet the spreadsheets we’ve been using have been this backward-looking technology that looks at that static data; they’re not forward-looking technology that really allows us to take the best view of the risk, be quick in time to respond, and be fairer to the client.

One of the most important things as a broker is service and speed in time to service. I’ve never heard a broker say they wish things were slower and wish things were less responsive. One of the things we have the opportunity to do is to take this richness of data, to mine the different sources—whether it’s publicly available, whether it’s customer data sets—to be much more ready to service our clients and to reduce turnaround times, to increase the quality and the breadth of the underwriting information that we feed back through to brokers and agents, to give them a clearer picture so they can represent their clients better. Operational efficiency is a bit of a buzzword, but it really counts to clients at the end of the day. The other opportunity is to differentiate better, to give information to brokers to understand so they segment and differentiate their portfolios better. That interchange of underwriting information has been relatively un-granular, and it’s getting more and more granular. The more information the brokers have, the better they can choose the right partners but also do it in a timely way.

One of the things that really differentiates insurance from many of the other markets is that you don’t really know the price of it until after you’ve sold it. If you’re making widgets, you know how much they cost to make, you sell them, you know how much the market is. Whereas in insurance, you have to form a view—you have to take some data, some facts and information about the world, and use that data to decide some beliefs about the risk. It’s not factual. It’s underwriting judgment and data and insight required to be formed about the risk being insured. You then use those insights to make decisions. You say, “Will I write this risk, or will I not?” Underwriting involves a flow from that data through insight into decision. We believe you can create a very powerful process where that flow increases the quality of the decisions that you make over time. You can create a feedback loop, and every time you underwrite and price a risk, or a broker binds a risk, the data underlying that gets richer. Over time, if you have the right technology helping you run that flow from data through to decision, you can get better and better at improving the insurance service to your clients. Pricing decision intelligence is that process applied specifically to pricing.

I was raised in insurance markets as an actuary at the front line, and I was always taught to think from three perspectives—and this applies to the whole value chain, not just the carrier side. You think about your expenses and your operational efficiency, you think about your premium, and you think about your claims. Ultimately, every stakeholder in the value chain cares about all three of those things, whether you’re someone buying the insurance, the broker or the agent responsible for placing it, all the way through to the carriers and the insurers.

One of the things we can clearly see with the advent of the software-as-a-service cloud-based technology that’s available to deploy in everyone’s phones is that it can make people very productive and can increase the speed and quality of the service being made.

On the other two sides, better pricing should ultimately result in fairer costs being charged to people. People who are higher risks should be charged differential premiums to people who are lower risk, and that’s better for the world more broadly because ultimately the technology then means that more people can be serviced. We’re seeing this in the MGA market where specialist MGAs—for certain types of health conditions or cyber cover, all of these specialist risks—through more sophisticated technology can offer broader, better coverage to people.

The last piece is claims. If you’re a good underwriter, you can manage better claims, you can have better performance. On the carrier side, you underwrite in a more sophisticated way, so you can price your risk and exposure better and have better profitability. Hopefully good-quality pricing technology enables the risk-bearing side to make benefits on all three key components of their profit and loss.

Hyperexponential was created by two actuaries working in the markets who were trained as software engineers. I always joke that we started in the market and we’re by the market and for the market—something we’re very proud of. But we came from the markets and crossed over to the technology side. There is a lot of technology that represents the experience we had as actuaries servicing underwriters and brokers for 15 years in the market. What we’ve done is embed that into open-source technology, the technology now that people are learning at schools and universities every day. The open-source technology we built our product on allows those people to have a very rapid learning curve. That drives two really big things, and when you combine them, they have a very significant impact.

One is time to market. A rapid learning curve, with a lot of IP [intellectual property] that does the heavy lifting for our clients, allows them to move much more quickly and, as importantly, allows very close collaboration between the teams that are trying to solve the problem of pricing, the underwriters, the actuaries, the brokers, agents, and the technology teams. What we see is, when people are collaborating, they make better decisions and the decisions can keep pace with the rate of change in the market and they can keep pace with the influx of new information available. That information provides a competitive edge. We’re big believers in the better insights and data you have, the better placed you are to make better decisions.

The bigger thing that we’ve seen is that the market is making fewer bets but it’s making bigger bets in businesses that it really believes are high quality. I’m biased, but this is my life’s work. People were talking about the Series B wasteland, but actually, what we saw was, if the business has attractive qualities, people were willing to get behind us. Competition for the round was very, very high, and I believe we were able to select the best panel of investors in the world for a business like ours. Our lead investor, Battery Ventures partner Marcus Ryu, co-founder of Guidewire, is now on our board. He is one of the legends.

Hyperexponential wasn’t selling a dream. We have delivered very significant projects for some of the world’s leading insurers, billions of dollars of premium, thousands of users, live on the platform in the market. Examples of real, tangible value are actually far rarer than [they] first appear, but not for us. We were able to prove that. Our business has always prided itself on being very cash generative and very capital efficient. Hyperexponential has always generated a huge amount of its own money through selling its products. The company makes its money last a very long time.