Indigo is a medical malpractice insurance company that is revolutionizing the insurance industry by leveraging advanced technology to streamline processes and enhance customer experiences. As the insurance landscape grapples with outdated practices, Indigo CEO Jared Kaplan highlights the critical need for modernization.
In this Q&A, he discusses how Indigo is addressing key inefficiencies, such as lengthy quote times and inadequate risk assessment, by employing automation and data-driven insights. With a focus on both insured populations and traditional insurers, Jared shares his vision for a future where AI not only improves efficiency but also transforms the way medical malpractice insurance is perceived and delivered.
Essentially, when speaking about the status quo, you have two issues. First, it's just a very slow process. It takes doctors days — if not weeks — to get a quote for medical malpractice insurance. Secondly, I would say underwriting does not leverage the best-in-class technologies that are available today to make the best risk decisions.
When you look at it, some companies have a tremendous amount of historical data they look at to figure out who's a good risk and who's a bad risk. But it's a completely different thing to understand what may happen in the future because we all know historical results don’t predict the future.
At Indigo, we are doing two things: we are automating the process, so it saves a lot of time and energy — making us the easiest platform to do business with.
Secondly, we recognize there's about half the space that pays too much money because they're not getting credit for their underlying risk profile, and there's two buckets of those customers. One is a customer who has been a very clean account for a very long time and is a great risk. But they're just paying too much money because they just haven't sought out what's available out there.
The other customers are those that recently had some issue arise. In those cases, everyone freaks out. We know it's a fluke issue because of the data we're looking at. So, we take a different approach and look at 2,000 attributes.
And based upon that information, we determine the risk of a claim in the future and whether they are priced properly for that risk. We just see it a lot differently than other companies in the space.
The issue is that when you're a tanker ship, it's really hard to turn. You have a lot of bureaucracy. You have a lot of hierarchy. You also have a very large team that has been wired to believe that the status quo works and that an automated process will remove the human element.
I saw this earlier in my career in small commercial insurance where I'd walk into a big insurance carrier and there'd be 30 underwriters who had been with the company for 20-plus years. They'd have this long application, and we would ask why they need all these questions.
There was a lot of resistance to removing questions from the application because everyone thought they were really important, when in reality, automated processes take in a lot more information with less work because you don't ask them to fill out a questionnaire. That’s why our platform gives you more predictable outcomes.
I just think it's hard when that mentality is so embedded, and people's jobs are at stake if you were to reverse engineer the process. But with us, we are new and brought on a team that fully believes in what we're doing. We were able to start off and say, “we don't need an application. We don't need a list of questions.”
Old school companies will figure it out over time, but it will be a while. We have a huge first-mover advantage. If we continue to get ahead of the competition, the harder we will be to catch.
I would say whether you're self-insured or whether you're buying insurance, you have to be unwritten. Just think about all the data that exists in the universe and what may or may not be relatable to whether someone ultimately has a claim.
We're able to gather thousands of attributes, and we're just scratching the surface of what may or may not be correlated. This process gives you a tremendous amount of insight at scale.
At the end of the day, we believe you can come up with a much more accurate representation of how someone should be paying for insurance based upon what their true underlying risk profile looks like. That’s just not possible to do with traditional manual underwriting. You need these technologically advanced processes to get a better answer.
We currently see pricing in the space to be quite narrow. In many cases, the largest risks are being underpriced because people want the premium. You really want to have a better segmentation process to identify the lowest risk customers. We believe we could do that at a much more accurate level than what's happened in the industry today.
I think, for our purposes, we have two customers. We have the doctor, and we have an insurance broker. And we want to be far away, the easiest company to work with for these two groups.
That means the submission process has to be easy. The service has to be easy, and the renewal has to be easy. By easy, I mean they should be required to give us limited information.
Everything should happen in real time with a remarkably high level of satisfaction because the customer expects that. This typically is not a fast-moving space, and everything takes a lot of time. I think we are already positioning ourselves to be the easiest to do business with.
We have a lot of room for improvement. And we’ll continue to evolve our automation infrastructure as an alternative to manual processes and ensure we can provide pricing and quotes swiftly.
I think there's going to be a massive movement in the next two to three years where companies will take current service models and leverage them with artificial intelligence. That means, in many cases, replacing the first line with a non-human who can do a better job because they know the answers to every question, and they can do it with both an epidemic and static style. At the end of the day, customers are happy.
Creating training models to best answer those types of questions and how you respond is a very straight forward process. Customers get the answers they need, and you should be able to do business in literally any way the customer wants to do business.
Whether that's email, text or chat, you can embed these technologies to do it much better. That is already happening in the marketplace today, and we won't be an exception to this trend.
We hope to employ these technologies so that no matter who needs us, for whatever reason, we can do a great job — a much better job than a human would do. If you don't adapt, I think you're going to get left behind because that's the new age of customer service.
In the past, people got very tired of these automated dialers and phone trees because it was very frustrating. But now, the technology exists to make those experiences very pleasant. And you get an answer as quickly as possible.
Any regulated industry is going to have a number of rules and regulations you must follow. But there are tools that allow you to set up rule alerts and controls inside an organization, so that when something is triggered, it can get flagged and you can look at it very, very quickly.
Insurance is one of the most regulated spaces out there both at the state and federal level. You have a lot of different governmental bodies that you have to report to, and you have to be accurate.
But we have the opportunity to build systems to identify when something falls out of an acceptable range of tolerance. Then you can have a human take a look at it. This makes for a very efficient process to stay out of trouble. We certainly will aim to do this, and many other companies and many other highly regulated industries want to do that as well.
We are using AI and machine learning tools to automate much of the submission, underwriting, and risk segmentation process while reducing the burden on our customers to do business with us. We are able to write insurance without an application or loss runs.
We can process unstructured data and provide real-time bindable quotes with a single data point (Net Provider ID) and identify opportunities for material customer savings. That's a huge game changer. It’s just inefficient when someone's filling out a 15-page application, sometimes with a pencil, and you've got a traditional underwriter who is looking at it and trying to figure out risk.
At Indigo, we can process unstructured data, and you could even send us an email you last sent to some other company, and we could take all the data we need out of it. There are no waiting days or weeks for a quote.
We can do it all in real time and identify opportunities for customer savings using alternative data and machine learning technologies to discover who is paying too much for the underlying risk. That's our whole secret sauce — automation and AI machine learning. And that is where the space is headed.
Indigo's new medical malpractice insurance coverage platform uses innovative technology to streamline the traditional malpractice insurance process. Now, physicians and medical groups can receive customized pricing in line with their true medical malpractice risk.
In the event of a medical malpractice claim, Indigo partners with local claims defense attorneys to defend you, allowing you to continue focusing on what you do best — looking after your patients.
It’s time for a more customized medical malpractice insurance experience that provides a high level of customer satisfaction and customer support tools.
Request your quote today.
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