The Golden Thread

FRASER:Number one is the industry itself. As we’ve gone forward with this, we’ve broken through the barrier of self-denial. There’s huge acknowledgement at the highest level in these organizations that the renewal process does not work for anybody—doesn’t really work well for brokers, does not work well for insurers, and it certainly doesn’t work well for clients.

We’re not trying to be disruptive. We’re actually trying to be more transformational and enable the current flow of data to be much improved, much more reliable, much more confidential.

Industry neutrality and the confidentiality of the data are basic tenets of what we’ve created. Everybody recognizes the enormous dependency on spreadsheets and the repetitive nature of the industry. It’s like a carousel: it just keeps going round and round. Every single year, risk managers and buyers have to start again updating all of their information—collecting it, validating it, sharing it. So much of that is just standardized spreadsheets that keep asking them to repeat the same information. A good example is, if a building was built in 1956, it was always built in 1956, but we’ll continue asking the same question for the next 40 years.

FRASER: I’m not sure insurers have lagged in investment in technology. I don’t think there’s a holistic industry technology investment plan that is designed to transcend the supply chain. What I mean by that is, if you look at insurance companies, they’re building out AI, whether it’s automated underwriting that will then become artificial intelligence. They’re investing huge amounts of money in that. Brokers, too, are investing in AI or in analytics; they’re investing in better client management tools to bring a greater level of efficiency. I do think the industry is investing in technology, but it’s in silos.

Insurers can invest in artificial intelligence, but if the data is still being sourced from spreadsheets, which create huge variances in quality, then they’re not going to get the return on their AI that they really need. If we can create one version of that data that is transparent and consistent across the industry—for client, broker and insurer—and therefore feeds the technology that they’re investing in, in their silos, they’ll actually get a better return from that investment, and that will be transformational.

FRASER: When we did the initial analysis three to four years ago with some input from some advisory board members and Mastercard, we estimated it then at $25 billion across the industry. That is the cost that could be extracted through the likes of a Big Ticket through industry neutrality and connectivity.

Where are those savings coming from? Currently, we’re repeating the same process time after time. Renewal times are long—for the typical middle-market North American business, it’s three or four months, at best, from start to finish. With the larger globals that I grew up working on, we would start next year’s renewal before we had this year’s policies in our hands and normally before the premium had been reconciled back into the market as well. So it really was a nine-month exercise. The amount of time that is being taken is ridiculous. It’s also a process that is very demotivating to huge swaths of people who are involved in it. You’re not going to find a client who likes the current process.

BARTLETT: The direct cost is one thing, but look at the amplification of other costs. Every insurer I know basically allocates capital against operational risk. A lot of that is driven by the fact they’re not confident about the exposure data sitting on their systems. People buy more reinsurance than they probably need to; less capacity comes to the market because of the uncertainties. So the direct cost is one thing, but you’ve got a whole series of other downstream issues here, from capital management through to the exposure data, which is the golden thread. Today, the problem of that data being in an unstructured analog format is frustrating the efforts of the industry to digitize core processes. So this problem, we summarize it in a nice, neat number because everybody can understand it, but it’s actually much more profound even than that.

FRASER: We’re doing this by reverse engineering, taking exposure data from the insurer, bringing it back into the broker, into the hands of the client, and then it becomes a 24/7 real-time risk registry. The client is then just using the platform to constantly update their data. The whole process of having to work on it for nine months, stop for eight weeks, catch your breath and then start again disappears. It literally disappears. You just constantly have the same data being updated in real time, typically being drawn out of their own ERP [enterprise resource planning] systems for example, and then it’s simply a case of it being released on a certain date and the renewal starts.

BARTLETT: Cybersecurity is one of the key benefits, which certainly the risk management community has highlighted to us. I mean, the efficiency play is important, but the very fact that sending a spreadsheet out in the clear today adds to your cybersecurity risk is sort of ironic. When I talk to industry actors, they sort of laugh, and then they go, “That isn’t very smart, is it?” So that’s a really key benefit.

If you take a typical risk, the exposure data ends up in between 50 and 75 systems on a single risk. And that happens sequentially today and piecemeal. We enable that to happen in parallel. You end up not only driving efficiency, but you end up giving a much better response to the customer in terms of service.

Today, the “dirty data,” the lousy data, amplifies its way through the system causing more problems downstream. The reverse is true when we start the stream with good-quality data, potentially in real time. The benefits are just increasing as the industry takes advantage of it and innovates on the back of it.

BARTLETT: The original thesis was that the insurance industry hadn’t digitized because there was no neutral infrastructure to tackle industry-level pain points and that no industry actor could deal with those on their own, no matter how big. Exposure data is a classic example of that: everybody in the industry knows the problem’s got to get solved, and nobody can actually solve it by themselves. So we start from that premise.

The second premise we start with is really based upon open banking principles, which is the data owner—the risk manager with the exposure data or the insurance broker or the carrier for their insights—they’re the ones who should control where their data sits, and, in the main, they don’t want their data sitting on a proprietary platform associated with any one industry actor. They want it on a neutral platform, and they want to be able to permission that data appropriately.

FRASER: We’re not trying to be the endpoint solution. We’re not trying to be the one that does the cat modeling. We’re not trying to be the one that brings the AI in. We’re trying to be the one that creates that golden thread of data. We’re trying to improve the consistency, quality, dependability and confidence level in the data to enable all of these other things to be done much better to the benefit of the entire industry.

If a risk manager’s got more time to spend actually internally improving the risk, then that’s a good thing for everybody. It’s good for everybody if the broker is able, instead of rushing into the market the first week in December, they can actually get it in eight weeks ahead because the whole timeline to get the data to them is much shorter. The quality and consistency [are] much better.

FRASER: Our focus today is the property line, but we’re very clearly going to be multi-line. We aim to be the one-stop shop for buyers to access all of their lines of insurance. We’re looking at standing up a second, maybe even a third line during 2024.

Where do we go next? After multi-line, payments. The reason why Mastercard is also interested is payment processing and payment reconciliation in the insurance industry is a nightmare for all participants. Once we have the digital rails in place, premium just becomes another data point, and who it’s distributed to is already determined by who’s on the risk, which is already sitting on the platform. Therefore, we can align the payment processing and reconciliation directly to the data that’s already sitting there by the participants on an annual basis and really move the money internationally at a much, much faster pace.