One Peak’s Post

Transcript

Bruno Nail adore, il n'y a quitt�� long Carrie, take Ta Company. IBM, Google, other intro text and we always wanted to start something by yourself. We got to know some people technique, so technical people, data scientists, actuaries that were working in an insurance company on specific use case of application of machine learning and AI on to actual processes especially especially pricing. And so we thought it would be a great opportunity to start a company. We saw all the benefits that I could bring to those actual processes that are heavily manual and that's basically how we started. The company. I think that from the beginning we've seen that the that was a global market, meaning that we could address with one platform, one solution, any insurance market, any insurance company across the globe. And so we decided to invest from the beginning to build a global company with different nationalities, being able to support any customers in any languages. So it was really in the DNA to address. The global markets, yeah. And therefore we, we have no more clients in more than 40 different countries across, across the globe. So yeah, we've been very international, quite, quite fast. Unlike like generic AI platform from from the beginning, we thought that for this specific use case, in order to bring business value, we needed specific algorithms. And the reason is the following. On this use case, as you know, insurance, insurance industry is heavily regulated. There is also high risk of adverse selection. So you need to have a very high level of transparency and control. And so none of the like black box effect of, of AI can be, can be acceptable for insurance for the insurance industry. So, so that was really the challenge it was to make sure that we keep full transition control. While using machine learning and, and this is, this is, this is how we started the company. So and and this is what makes us what makes us different and still today is this capability to have applied AI on a very specific vertical, vertical use case. Taking into account the the the requirements of this of this of this use case, which is insurance pricing. I think this is this is really key to bring business value on vertical to make sure that we are not just applying like generic AI, but we are making sure that the the algorithms, the machine learning takes into account the specificities of each. Yeah, business processes. So for the insurance industry, it means that you do not have to do a trade off between performance and transparency anymore thanks to the technology and these algorithm. But you can have better models in a transparent way with transparent output. So that's really an innovation on that, on that specific use case and for the industry. But I create it's not only algorithm and we are convinced that when you want to leverage AI, you have also to build processes and a solution. With some functional features around the algorithm to make sure that you can really leverage these these algorithm in production for a business purpose. So it was also along the process to develop the algorithm but also to develop the solution and the processes around the algorithm that we have. Yeah. And and we do algorithms, we are not like fully automating the the process. We are automating all the data-driven part of the process. But at each step of the process or users which are actuaries are going to take decisions. But given we automate this data-driven part, they have much more time for analysis and and for for spending time on taking the actual business decision instead of yeah, doing repetitive tasks that can be that can be automated. And really I think that we can say that how ambition is really to provide the right technologies to the actuaries to help them to take better decision in a faster way. So it's really how to leverage technology for business objective. We recently completed our first acquisition welcoming the various team within accurate. So Arius is a is a reserving solution. It means that we will that we are enriching our product portfolio with our reserving solution. Always the same DNA provides to the actuaries the best solution to ease their job and to support their business processes. D'un business objective so it's a way for. Us to expand our actuarial platform with additional use cases and to provide to the insurance industry better technology that they can they can leverage. So it was like a very interesting journey to to run that acquisition and we are already super happy with the the first discussion that we have with our customers, the integration of the team within accurate and the dynamics that we see on the market. Yeah, it was, it was from the start or vision to build a global fully integrated external platform leveraging machine learning and and so yeah, AYUSH was an opportunity to accelerate on that vision. It's not on your product. It's a very experienced and highly skilled team and also more than 140 clients heavily centered on the on the US So that was a unique opportunity for us. To drive more value for clients and we see significant traction on the market, we already decided to sign some sign some, some combined partnership with clients acquiring both reserving and pricing at the same time. So yeah, very, very positive market feedback. We continue to, to, to to expand our platform to deliver additional, additional business value to our clients. We have already released 4 modules. We are releasing some new modules optimization that was just released, so allowing to find the optimal commercial prices. Which is having the capability to to put your prices into into production. So it's like the last piece of the of this of this process. That we are and we released this module on the first quarter. Next year we started to work on a yeah, new and very different differentiated module, which is called the rate repository, which will be kind of the missing link between commercial tariff and and and the production production engine. It's a module that will release end of next year. And then there is of course our use as well on which we invest a lot in terms of transformation. To embed more machine learning to fully migrated to migrate areas on the cloud. So yeah, still investing massively in R&D and developing our platform. And I think that of course, we will also continue to expand globally and to yes, to increase the size of the team to be able to support more customers across the different geographies that we have. So still lots of work in front of us and. The large opportunity that we see for the future.