The short answer is in many ways, and for a longer answer, I would group that by technology.
Predictive analytics and machine learning
Natural language processing
Generative AI
Computer vision
On the topic of predictive analytics and machine learning, we are seeing that used in tariff building, risk selection, and very individualized rates. Another topic is natural language processing, which we see in submission intake, or claims report intake, for example, for understanding hand-filled documents and preparing them for the claim handler. Then generative AI — the biggest buzzword right now — is being used for customer service at MGAs. We see that a lot, ranging from simply improving FAQs to fully functioning chat bots, and even bot-based phone conversations. The last thing we see, which is a bit more specialized, is computer vision to review videos or images in property lines. It’s used, for example, to understand the condition of a building or the features of a building. Those are the areas where I see AI right now.
Where do you think AI has the best application for MGAs (e.g., underwriting, portfolio management, etc.)?
Expediating claims process
Risk identification and selection
Deployment of AI agents
Mainly in three areas with the most exciting, vast application, and very immediate is in the claims area. We see a lot of innovation and a lot of value generated by speeding up claims processing and creating less effort for the claims teams, which translates into higher satisfaction for the insured. The second is risk identification and selection. MGAs are successful in using AI with new parameters to identify the good risks in a subset that otherwise would be left on the table. So that taps into underserved markets and helps the MGA provide insurance for those. And the third one is probably more something of the future, and that is planning for AI agents. You can, as an MGA, deploy AI agents as your distribution. But another even more exciting area is the question of: What if your customer, the insured or the broker, is using AI agents to automate their part of the communication? How do you prepare as an MGA to communicate with an AI agent on the other side? Does that mean you also deploy an agent or create a completely different front end for your company and distribution? If an MGA gets that right, it is an awesome application and will create a lot of value.
Does Munich Re make any investments in AI-heavy MGAs or AI companies currently?
Of course, the Munich Re Ventures team, our venture capital arm, is the best team to answer this, but they've made a bunch of investments in AI-focused companies, including MGAs. Focus areas are insurtech and cybersecurity, amongst others. Many of the existing MGAs in their portfolio are also now incorporating AI more and more. So that's an investment area for these MGAs. We work closely with the Venture Capital team to identify opportunities. I’d also mention the acquisition of Next Insurance through Ergo of Munich Re Group, which happens this year. Next Insurance started as a very automation- and digital-heavy MGA and grew very successfully. My team has been working with them for many years, so we’ve very excited about that big step. It's been a great success for us.
Where do you see the greatest opportunities for Munich Re Specialty in this space?
I would say many other carriers or capacity providers are shying away from startups. They are companies that say, “I do things very differently. I fully rely on technology here.”
We are actively looking for those. Simply, our risk appetite here is an opportunity. I would also say, insurtech now is really changing. Insurtech started as bringing insurance on the internet, and now we're seeing a next wave where insurtech fully focuses on AI, IoT (internet of things), and many other technologies. That will likely bring a lot of exciting business models that will scale fast and successfully. So, if we work with these companies, then we tap into that success.
What are the challenges for MGAs to implement AI into their operations?
I’ll start with an advantage, which is that they have very limited legacy systems and processes as an MGA compared to other, longer existing companies. But one challenge is, of course, your partners in the value chain — carriers, reinsurers, brokers — value AI use cases differently. The big challenge here is the balance of how much human touch, as an MGA, is used. And how usage between AI and human referrals is optimized and how my partners respond.
Another big challenge in using AI is the attention. People love pointing out the errors of AI. So that means when you deploy AI, you need to make sure it is done right and it needs to work right from the start. That also applies to MGAs.
What sets digitally savvy MGAs apart from the rest, and what common traits do successful ones share?
Technology is moving very fast. We all noticed that at work and outside of work, if you look back two to three years on the AI side. AI as a science, and as a field of mathematics, has dramatically changed and so have the tools that are available.
Successful MGAs are the ones that can stay on top of new tools and technology, as well as that are capable and not too shy to use these new developments — not new developments from six months ago but the new developments of right now. A key driver is very often the right staff at the MGA.
As we see larger carriers investing in AI, what are the advantages of working with MGAs?
For large or long-existing carriers, they first have to focus on legacy systems, processes, and culture, while an MGA can build out AI fast with limited friction. Working with these companies is really an opportunity to diversify and profit from developments on the tech and AI side without fully disrupting core (re)insurance business. On the other hand, a larger carrier might have the opportunity to tap into bigger resources when building something and tap into potentially bigger data pools internally.
Since AI is all about data, shouldn’t long-established, big carriers be the best positioned to build and roll out AI models, compared to a new MGA?
It very much depends on the carrier, and especially on their setup over the last couple years in how they set up data strategies and data management to have that historical data available. When it comes to pricing, it's extremely helpful to have a great set of historical loss data, which long-established carriers like Munich Re have. On the other hand, if you use AI, the great impact comes from using new parameters, and these parameters are often not recorded in that historical loss data.
One example is telematics. You can have many years of auto accident loss history, but you probably won't have the telematics data from five or 10 or 20 years ago, so you cannot really use that historical data to build the driver score correlated to losses. The good thing is, data sets are becoming more and more available, also packaged in a way that is easy to digest, and connecting data sets is becoming much easier from a technological point of view. It’s an opportunity for both long-established carriers as well as MGAs.
What advice would you leave our readers with for those interested in partnering with an MGA?
I would say, don't shy away from trying out tools or understanding why an MGA is testing a new tool or forging a new path. They might know something or understand something that really could create value for both the customers and the carriers.
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