 Yeah, question to get us back into the swing of it. The one thing I saw on your blog was the talking about FinTechs and especially startups. And the question is, should entrepreneurs be focusing more on collaborating with the established players, or should they be going for the dream of disruption and trying to do the entire process on their own? What advice would you almost give to new entrepreneurs coming into this FinTech space? So I think you raised a valid point about how FinTech entrepreneurs should position themselves. I think it's also well documented that over the last few years, there has been a trend from disrupting and moving towards collaboration. I think the trend is well documented, so I won't go into that. I think whether you choose one or the other depends really on the business model and the specific offering that you're bringing to the market. There may be offerings that are a specific component of the value chain, where then collaboration is perhaps the quickest route to market. Because obviously without the other parts of the value change, it will be quite difficult to monetize that part of the value chain on which you are innovating. I mean, I think to be perfectly honest, I think the biggest challenge is not that FinTech innovators want to collaborate with incumbents. I think the challenges of incumbents may not be fully ready to collaborate with the FinTech innovators. And I think that's where my suggestions to the incumbents about, well, think about what's your infrastructure. Are you ready and get ready to be able to engage with those guys and be able to bring the technology that you need into your business? Well, I mean, this is one of the things that I think the data scientists are getting a little bit annoyed with is they're going through the big data and they're finding some interesting correlations. For example, the one thing that they discovered was that lapse rate was connected to the premium policy date as well as when that person earned their salary. You can imagine, if someone gets their salary and then it's their premium date, it's going to, I think we've got Kiara back on the line. Fantastic, good to have her back. But yeah, the idea was that the lapse rate was connected to the premium start date and connected to when that person received their money. If the person received their money and then they had to pay it right after, they were less likely to lapse. Whereas if they received their salary and the time to pay the premium was like 28 days later, they didn't have any more money at the end of the month, then the lapse rate was a lot higher. And so they thought, you know what? We found this great opportunity to improve on the business, reduce lapse rates and make money. The problem was is that the big insurers were like, this is great and all, but we actually can't really easily make a change to our product where we now start asking people when their salaries are and move the premium payment date because some of their legacy systems had anchored them to always make the payment on let's say the first of the month, irregardless of when the date is suggested. So it's one of the interesting things is sometimes we can find some amazing discoveries, but the legacy systems aren't flexible enough to allow us to actually reap those benefits. So it's, I don't know, in your opinion, do you think this is maybe one of the big reasons why especially the large corporates need to do data transformation and, you know, embrace digital systems and the new ones as soon as possible so that they can get these competitive advantages? I'm not entirely sure. I mean, I think the scenario you describe is a good one in the sense that it reminds me about one of the strengths of the insurance industry. So the insurance industry has been very good at optimizing and what I mean by optimizing is making small changes that can have a value to the business. Perhaps this one is perhaps one that is overly complicated because of the legacy systems and it's therefore not a possibility. But so I think the wider point here is that the challenge that the industry might be facing is that it's running out of all the opportunities for these small changes. And then once you run out of those small changes, then you have them to think about the bigger changes like more significant data transformation or a serious look at the legacy systems.