Nick: Hi, everyone. Welcome back to another episode of Reinventing Finance. As usual, I'm not all by myself. I have my lovely co-host with me, Tom. Tom, how are you?

Tom: Yeah, thank you, Nick. I'm very looking forward to our conversation.

Nick: And as usual, we're not all by ourselves. Today, we managed to get someone from a little bit further eastward. And we have with us Saeed from Desaisiv. Happy to have you on our show. How are you?

Saeed: Likewise. Thank you very much for inviting me. And I'm actually excited to share my talk with your audience and yourself as well.

Nick: Of course. And thank you for taking the time and sharing your view. But maybe for those of our listeners who haven't already clicked through and checked out your LinkedIn profile, why don't you briefly introduce yourselves and tell us who you are and what you do exactly?

Saeed: For sure. So as you just mentioned, my name is Saeed Khawaldeh and I'm the co-founder and CEO of Desaisiv. Desaisiv is an insurTech platform which operates in the Middle East. However, it is originally a spin-off from the University of Oxford, where I finished my PhD over there in artificial intelligence. And talking about Switzerland and Germany, I've actually like studied at Tübingen in near Stuttgart, a very lovely small city. And mainly like I was trained to be an engineer. But for nearly like eight or nine years, I've worked in various management positions at different consulting firms, a few corporations, including Microsoft, and as well, a few SMEs and start-ups. Mainly, I was based in the UK, where originally we kicked off Desaisiv. But since the gap, which is available in the MENA region, when it comes to the insurTech sector, we have started working specifically in the Middle East and focus at the Saudi Arabia market.

Tom: Understood. And you're based now where?

Saeed: Riyadh, Saudi Arabia.

Tom: So you're sitting in Riyadh now.

Saeed: I am technically actually at the moment in Jordan, in Amman, where we have most of our technical team. However, I'm flying in the morning to Riyadh.

Tom: OK, Jordan, OK.

Nick: Awesome. So you've already alluded a little bit to it, but we always like to also understand the person behind the product and behind the company. If you're kind of looking back on your career or the kind of traction of the company so far, what were some kind of Desaisiv moments, some milestones achieved that you kind of look back and say, this was pivotal, this was relevant to me, and maybe even some learnings from that before we kind of dig a little bit more into Desaisiv?

Saeed: To be honest, when we started Desaisiv, myself and my co-founder, Mohammed Nabhan, we were mainly working in the healthcare escape. We were trying to help hospitals in optimising their inventory by predicting their supply chain and making sure that they get only the supply which they need for the patient flow that they have. However, that was quite a product which was a bit like in the future when it comes to the market where we were trying to serve the Middle East.

They were like a bit behind when it comes to optimising the healthcare system. So obviously, as part of the journey of any entrepreneur, you need to keep pivoting to see basically what's the problem which is worth solving and where the customers, they're actually wanting you to solve that problem. We've reached that specific milestone which is discovering that not only insurance, actually like corporates, they're actually suffering when it comes to the relationship with the insurance companies, when it comes to health insurance, and they want a bit of support when it comes to dealing with insurance companies so they can get the best benefits for the best price.

Here, we've just decided to build a product that fits their needs. We were quite lucky that the conversion rate for our customers was quite very high once we have developed the product reaching nearly 5% of the Saudi market share when it comes to the health insurance. Obviously, this is a massive milestone in any entrepreneur life, especially when it comes to the company, like you building a product and then over than 500 different companies are using this product. This is quite a validation when it comes to the importance of such product. So I believe this is probably the most important milestone in our journey as a company. Obviously, on the personal level, I always was fascinated about artificial intelligence.

Prior to Desaisiv, I really wanted to do a PhD where I was focused on machine learning and building these algorithms for commercial use. Probably, the biggest milestone which I have achieved on the personal level was to be part of a big project where we built something very interesting, got at the end being sold to one of the commercial companies to go and commercialise it to be used in one of the system which is called deep brain stimulation. It's something which is very important for the Parkinson patient people.

Nick: Awesome, awesome. You've already mentioned a little bit. Can you walk us a little bit more into what you guys do at Desaisiv? And maybe we'll start at what is the problem that your customers, who are your customers and what problems do you solve?

Saeed: For sure. So in Saudi Arabia, just as we're focussing on this market at the moment, the corporates, they are having an issue when they want to renew the health insurance policy for their employees. The main issue that they are dealing with that the premiums get increased a year after another year. Sometimes the increase goes to 50% increase of premium. And that is obviously linked to insurance companies doing monitoring for how the employees are interacting with the insurance policy, calculating the loss ratio. And then at the end of the day, obviously like trying to make a healthy profit for them as insurance companies. And at the end of the day, the corporate, they're actually the ones who are paying the premium at the end of the day.

So we have identified this gap in the market, which is monitoring specifically how the employees are interacting to the health insurance policy. And then understanding very well the usage of the insurance policy by the employees of any corporate and doing as well negotiation with insurance companies so they can get the best value for money. Because very often companies get insurance policy with multiple benefits or medical network. The employees are actually not using most of that medical network. So Desaisiv goes and customise the insurance policy details in a way that it can be leveraged to the maximum by the employees of each corporate. And at the same time, the corporate can pay the fair price for this health insurance policy.

Nick: I'm not an employee benefits specialist and certainly not in the Saudi Arabian market, but I would, I would assume, isn't that the job of one of the big brokers, commercial brokers that take your current programme, makes a tender to the different insurance companies, has a view about how to manage premium costs and potentially also recommend on which type of benefits are likely to be kind of taken out. Does that not exist in Saudi Arabia right now, that type of intermediary?

Saeed: Great point, actually. To be honest, we are working with a few brokers as partners in Saudi market. And these brokers, they're actually like one of a few, like our most important partners to reach like our milestone when it comes to market penetration. However, when it comes to Saudi market, there is a quite a unique dynamic for the health insurance policies and how obviously the government regulations is actually regulating the health insurance for the different employees. And one of the things that we have like missed as we have found out as a gap, brokers are actually like very much doing quotation comparison, getting basically different quotations from different companies and just summarising it for the customer without going into too technical details on how to customising the insurance policy.

Okay. So like, I mean, Ion and Marsh, obviously a few of the biggest players when it comes to the brokerage, they're actually operating in Saudi Arabia. However, a few of their customers actually, I don't want to disclose the name, but it's a policy which exceeds a hundred million dollars yearly. They're working with one of these biggest brokers and they're using as well, like our platform, Desaisiv platform, because like the platform in itself, it's designed to help HR or health insurance policy at the corporate in a way that it can simplify the very complicated insurance policy language, which insurance company and the brokers speak and corporates don't speak in a way that it can be like, like of assistant to these HR managers or like the health insurance officers who are dealing with health insurance for the entire policy. So like they can have at the end of the day, a simplified version of the health insurance policy that they can interact with it. And as well influence in a way that Desaisiv is offering that platform to outsource the entire health insurance policy.

Let's say renewal for these different corporates. So I completely agree with you. I've worked and lived in multiple countries, the US, the UK, and even like a few European countries, and probably the channels between insurance companies, healthcare providers and corporates are more digitised when it comes to the developed countries, which makes the broker work much easier.

Nick: Let's see. I don't know. I wouldn't bet on it.

Saeed: Okay. Well, I mean, because like, I mean, I'm comparing it to the NHS in the UK, for example, all the data is available publicly if the client gives access to whatever broker to get that data. However, when it comes to the data, like, you know, handling and data availability and data cleaning, all of these different issues, when it comes to the Middle East or Saudi Arabia, like which is the market we're focussing on, there is still area of growth in there. And I believe Desaisiv and what we have built as a technology, it actually helps bridging that gap, obviously, till the market is more digitised and the processes is easier so that, you know, let's say like different stakeholders are actually like contributing in a better way to the benefits of the corporates and their employees.

Nick: I think at least for me and Tom, you know, please interrupt if you've already figured it out, because it's maybe for me, it would be quite helpful to just kind of, if you walk us through the step by step process, maybe even without AI, without automation, just these are the steps that a poor human would have to do in order to come to a similar insight of understanding usage, customisation, et cetera. And at these steps, we've injected technology and, you know, sometimes it's just its AI, sometimes it's not AI. So just to kind of understand the kind of steps required to create that benefit. For our current customers. For our current customers. So, you know, I'm a customer, I have my employees; I have this insurance policy, right? How does the process look?

Saeed: For example, you're like a customer, for example, you're the HR manager of a company with 5,000 employees. Yes. And you have two documents that you have always available for you.

One, something we call a claim experience. And the other one, it's called active list. The active list is simply the list of employees that you have at the company with their details, ages, all of these different things.

The other one, which is the claim experience, it comes in form of PDF, sometimes a scan, sometimes photos of how your employees interacted with insurance policy the previous years, up to three years back. So what happens, you get these two documents and you simply throw them at the platform that we have built for you. Simply you login; it's an old application, very user friendly. The platform, like, gets all the different details, which is related to your health insurance policy using some computer vision and OCR, in addition, like, to some data processing. So we learn what is the medical network that you have in your health insurance policy? What are the benefits? What is the usage for the health insurance policy over the years? Like, for example, the list of employees and their details, all of these different details, which was important. And then it just summarises it in analytics, like various figures that can make it easier for an HR manager to understand their insurance policy and how they have been interacting in the previous years.

Once that process is done, obviously, like, we, part of our, like, understanding for the market, we understand very well how underwriting process happens at the insurance companies. We have expert underwriters, like, from the Middle East and Saudi markets specifically, in addition to some machine learning models that do the prediction for the completion of the claims. For example, if you supply six months of a claim experience, there are other six months to predict. We do prediction for that. So once that is done and the prediction is done, we do prediction how much your health insurance policy will be costing you next year. And this is quite important for you to do budgeting for the health insurance renewal and to understand as well very well what benefits you have, you know, you have used, what benefits you don't use, et cetera.

Then we do recommendations as part of our offerings, where we actually tell you that based on your data and the documents, when it comes to claim experience, it comes in forms of hundreds of pages. And it's quite complex, like, to go in every single one of them, unless you're an insurance expert. But when it comes to corporates, they don't have insurance experts working with them. They have HR people. And what happens, the platform by itself that uses different optimisation algorithms, which suggests what are the benefits, the medical networks, the coverage, the co-payments, the details which are related to the health insurance policy that fits your employees in a way that you can get the best benefits for the best price, the best value for the best price when it comes to the health insurance policy. And finally generates it in a document or in a form of an entire insurance policy. And that can be, let's say, the baseline that you can use when you go to the health insurance company to tell them, I want this specifically. Obviously, at the end of the day, our model uses various underwriting methods in order to come up with these different numbers. And that actually helps the negotiation to be more transparent and much easier between the HR person, like an HR manager, and let's say, like the underwriting person or the insurance company, which is quite like a complicated process at the moment, because what happens, you submit your two documents and you get a quotation.

To be honest, very often we have many customers who are HR managers; they don't understand the complex details inside this quotation. And what happens, okay, they push on reducing the price by limiting a few things, but you're not going to reach at the end to optimised way of getting exactly what you're going to need. And that's what we're aiming at.

Nick: So I'm just trying to play this back. So one step, you have a list of all your employees, you get some form of reporting from the insurer about the usage of the policy, which probably has a, and that, for example, I'm not sure whether that is something that you could even share in, for data privacy reasons in Europe. I assume you have medical record, medical procedures, medication, et cetera, on the policy on a per employee basis, I guess.

Saeed: I agree with you about the sharing a bit. I mean, maybe just carry on and then I'll just comment on the data protection.

Nick: And so I think one of the steps is obviously extracting this information into a database, right? That is relational in some form. Then you, because of some experience, you can estimate future development because you only have a timeframe. You've mentioned six months, what happens to the next six months. Now, in order to map that towards, you then would have to have built yourself some form of model around the types of coverages available in the market, some estimated price points from previous quotes or a retrofitting of your own underwriting engine to then kind of say, so do you like recreate the products like price comparison websites do of the existing players in the market? Is that what you need to do in order to match their existing usage towards products in the market?

Saeed: Yeah. Well, I mean, because we're dealing with corporate insurance, it's quite very difficult here in the Middle East market to get like a quotation or like instant quotation. It's not possible. So the instant quotation is available for individual insurance and for small enterprises where you can book rate. However, when it comes to us, we're actually focussing at around 85% of the market, which is big corporates. When it comes to that, our way of like what you present to our customers is indicative of pricing of their fair price when it comes to their health insurance, if they want to renew it. However, it does not actually show the difference between different insurance companies. This is our current phase. We are actually like doing integration with multiple insurance companies. So the entire renewal of the policy happens through the platform. So it is like more like an aggregator for corporates. And this is a phase of development that we're going toward at the moment.

Nick: And because AI obviously has been a buzzword with the advent of Gen AI, now everyone... How much of it is, where does AI really play its part? Where is it just statistics? And to me, and please, I don't have a PhD in that kind of stuff, but to me, the difference is kind of where you don't exactly tell the model what to do and what to spit out and where you just kind of let it run. But in what aspects of that product or what aspects of that product would not work without injecting AI? What would be prohibitively more expensive to do? Yeah.

Saeed: Let me just answer this question after talking about the data privacy. I agree with you very much. I mean, like sharing the details, which is linked to individuals is actually like prohibited, even like in the Middle East market, you cannot share details which are related to Nick or Tom or Saeed, because obviously that's privacy when it comes to our personal data. However, we do fully anonymised data when it comes to the active list, which is the list of employees and link it to the medical data that is available in the claim experience. However, we don't know who these people are. These are the employees. It's anonymous numbers. And like mainly like...

Nick: Okay. So the medical data is not on an individual basis. It's just there's been 20X of medication ordered, but it's not actually linked to an individual, is that what the data?

Saeed It's anonymous, exactly. And like, you know, when we kicked off the company, we hit it off the UK and obviously the GDPR in the UK is quite complex and it's quite a baseline for multiple even countries and, and, and regions of the world. So we've just been very sure like to adhere to that, which by eventually like made us like, you know, like when it comes to data privacy as well, adhering to the local authorities and their data protection, like, you know, a guideline.

Just coming back to the AI question, uh, Nick, to be honest with you, like, yes, I have a PhD in machine learning and probably like seven or eight of our employees, they have as well, like PhDs in machine learning. However, we're not the type of company who wants to build a very fancy coat and then make it like mix, like make me, for example, like a person or like a specific, like an organisation where it just because it looks good. To be honest, we care about something which actually serves the customers. However, in the current platform, the OCR and the computer like vision thing, it's quite important that it is really efficient and the data comes in Arabic language and in English language and comes as well scanned sometimes when it comes to the OCR, it's very mature for English language.

I can see like a lot of examples, which are quite easy and plug and play when it comes to the Middle East and the different languages, I don't think the models which are available publicly, they are really doing good job. And we've like put a lot of efforts in, into building something, which is robust works at all the templates and all the formats which are available in the market, being robust and always giving very good accuracy when it comes to the data reading and the other part, which is quite critical for us to use machine learning, when it comes to the actual underwriting experts or actuarians, they're actually using forecasting models to predict or like to estimate how much you're going to cost the insurance company after six months of, of your, like, you know, current six months bar. However, that forecasting it's statistics, it's not really like predictive modelling when you compare like the accuracy, maybe it's 70% or 80%, and obviously like when you use some predictive modelling, which is a bit more accurate, it's fitted to the data and the specific behaviour of your employees, then you can just go on, get something like 96% accuracy, and that's quite important for you to get a better pricing and more indicative, the right pricing for how much it's going to cost you to renew your insurance policy.

So these two elements that are quite important in addition to the optimisation bits, when it comes to multiple, like there are several factors there, they are very important in any health insurance policy, the co-payment, the coverage, the medical network, the classes, the distribution employees over classes, there are plenty of factors using a right, a very good optimisation algorithm, which find the best parameters and the best distribution of these different parameters to give the best price is quite a challenge. And we use a really like some, uh, cutting edge machine learning algorithm that we have built in-house for doing that optimisation bet, which is quite important to achieve the end goal that we're trying to achieve for our customers.

Nick: Understood.

Tom: I have another question that's perhaps less technical, because we don't often speak to people who are working in the Middle East, which is fascinating to do, what are the biggest differences? Because on the one hand, we always have the idea that if we talk about, let’s says, building platforms and AI and algorithms that it's, it's everywhere. It's exactly the same, right? You can use Facebook or Google and everybody probably has the same function on this screen. But what are the differences in the, let's say for you, as you know, all those different markets, you know, the UK, you know, the U S et cetera, what is different in, where you are active now?

Saeed: Definitely for sure. That's a great question, Tom. Thank you. Uh, to be honest with you, like I've just done like different work at multiple companies where our focus markets were Europe or UK or us, as you mentioned. And surprisingly, whenever we deploy any model, it's quite, you know, it works well. Things are quite straightforward because very often like these different models, they're getting trained at data coming from these different geographical locations. However, when we just obviously like started working in the Middle East, we've discovered that there is a massive gap when it comes to the data availability that publicly have access to, and, and it does not, and it represents the Middle East market, Saudi Arabia, United Arab Emirates, or like other big markets. And surprisingly, uh, Nick and Tom, it's quite a big market. So for example, in Saudi Arabia, when it comes to the health insurance, we're talking about above $8 billion of health insurance premiums, the total. It's quite a big market when they compare it, for example, with Pakistan market, like where it's like nearly like 230 million people and the premiums over there, when it comes to health insurance, it does not make around 20% of what, what the premium getting paid in Saudi Arabia for from 30 million people. But government there makes health insurance mandatory for all people who are living there and makes the market quite interesting.

And obviously coming back to the differences, the data availability, the data type, the language used, let's say the models that they are trained to be used for such data, which is available in the Middle East, all these different things that actually playing part at, at making the challenge, uh, harder, like to, to basically like build something that really works for the Middle East market. And obviously one of the most important things, the regulations here, regulations as like, we're talking about country, which, which has a lot of changes, uh, over time, uh, positive changes. Uh, I mean, as I observe it, I can't see regulations. They are actually moving quite fast as well. And to like build a technology that can work like in an adaptive way to these different changes that happens over there, it's quite like a challenge when it comes to like our sector and what we're focussing on.

Nick: Would you, because if I'm, just playing this back, one of the, the training sets are different, the language to the alphabet, right. And everything that that entails is different. And by nature of working with large corporates, based and if you do a job, well, they start to suggest certain problems that they might have, which problems have you already been requested, that currently not on your roadmap, but that you could also empower with your, you know, trained model that works within your geography and in a kind of regulated space, and with the guardrails that someone would need in order to start feeding that type of data into a model. Have you had certain discussions that are around that topic?

Saeed: Definitely. So, so we'll be very focused on health insurance problem solving, and that's a very robust model that, and like a platform that serves over than 500 different corporates in Saudi Arabia. Uh, however, one of the, like few of the major requests that we received from these different corporates that we work with, that they want to have one-stop shop, they don't want to buy health insurance from us and then go on like general insurance somewhere else or motor insurance somewhere else. So like they are bringing, be requesting to do the same exercise for the other insurance types. And that was not really like one of our priorities back then. However, recently, I mean, we're raising a series, a pre-series A round at the moment. And one of the, a few of the main, like, you know, important things that we're going to be working on is to expand our offerings to go towards motor insurance, general insurance, life insurance as well, because the, the, the markets are growing here when it comes to it. And it's quite, like important to take whatever learning experience we have had with health insurance and map it to the other insurance, insurance types that makes the offerings more likely, let's say, concrete to our customers and, and like make them as well, like have all their needs when it comes to outsourcing the health, the insurance policy for all the different types in one place, which is hopefully Desaisiv.

Nick: But it's still, I guess what you're saying, the assets that you've built one is that great access to 5% of, you know, these 500 corporate customers, plus a localised OCR data cleansing, interpretation model that is fit for local regulation. Is that a fair assessment that those are two of the kind of key, key assets?

Saeed: Exactly. Plus obviously taking into account all the data protection pipeline, we're actually like; we're building like a unique data set that is actually representing the markets that we're working in, which is very important, like for ourselves as a company operating in, in, in this market and as well to partners to utilise this data, which is fully obviously anonymised and it does not violate any, any privacy, uh, regulations so that, that we can actually, let's say, elevate the level of maturity when it comes to the technologies that being used in the markets that we're operating in. So that's another thing which we're actually focussing on and we have it as one of our key milestones.

Nick: But I guess it's fair to say that there is more opportunity as you currently see it to expand, let's say, insurance products and services within Saudi Arabia, then trying to export the current employee benefits thing into other Middle Eastern countries. Is that a fair assessment or can you do both?

Saeed: To be honest with you, there are markets which are similar in behaviour when it comes like to Saudi Arabia, for example, Oman, it's one of the biggest insurance markets in the Middle East, and it's quite similar when it comes to the Saudi Arabia market, United Arab Emirates as well, that's a, like a third interesting market and Iraq, these four different markets, they are big in size and the behaviour of the insured members and, uh, corporates and all of these different things is like more or less, you know, uh, I would not say identical, but similar to each other, so we're thinking about these markets for expansion, but for now we're focussing at the Saudi market, because as you mentioned, we've just like acquired 5% of the market when it comes to our current offerings. And there is still plenty of, uh, of like fish in the sea. We can obviously like expand our offerings. We can get more customers and we can as well maximise like the benefits for our customers by actually offering other services for them.

Nick: Absolutely. Absolutely. No, no, I was just thinking that there is, and that's the nice thing about a corporate B2B play, is that if you get that trust, they'll kind of nudge you into certain directions, also a risk, but, but they'll present opportunities, even pre-product sometimes. And when they'll be, so for example, I would not be shocked if some of the insurance companies would say; it would actually be quite interesting if you, we could just because all of the existing policies on paper and other, and it would actually make our quotation process a lot easier. It would actually help us in our quotation process, kind of knowing what the existing insurance policy looks like and that in and of itself could be a great market, and business development opportunity. I'm just kind of conscious of time a little bit here and I wanted to kind of give you the room. Is there anything that we haven't touched where you felt we fell a little bit short in terms of our questions that you kind of want to share, then now would be a great time.

Saeed: For sure. I mean, like actually following on your last point, which is, uh, one of the things that we're actually like, you know, exploring where we're at the moment, uh, exploring the full integration with insurance companies, where we can make the process easier for our customers instead of them, like getting that indicative pricing. And I like getting, for example, certain like guideline for them, like to do better negotiation when it comes to their health insurance policy; we're actually trying to make the complete the cycle to them by integrating with the insurance companies.

And as you mentioned, like building the technology in a place, uh, actually helping us in doing that integration, which will help us as well in making the customer journey much easier. And this is really like our goal at the end of the day, we want our like corporates to have a better experience when it comes to renewing their health insurance policy and ultimately like help the employees have like really what they need when it comes to the different benefits they're having and maybe the same medical network. So they don't need to get like, you know, disrupted by changing the provider of the health insurance, which actually happens very often. Obviously looking for the best premium. And one of the things that we're doing, actually  we believe that we have a quite strong capabilities when it comes to our tech people, to our like team that works like to serve the Saudi Arabia from the UK, Jordan, Egypt, and even Saudi Arabia. So we have a simple, quite a strong technical team, obviously.

And we have brought like a few of the best cutting edge algorithms that have been built, let's say like in the UK and abroad, and just we've customised it and localised it to the market that we're operating in. And this is quite strength when it comes like to what we're actually building at the moment. So we're exploring even the projects with our customers, as you mentioned, like few of them have actually asked us to do projects when it comes to analysing their data, to help them with AI algorithms. And these are things that are quite like motivating for us, showing that our customers are actually valuing what, what offering we're giving them. And it makes us as well, more interested, like looking at other products that we can expand to and just offer as well, like to our current client base and the future ones as well.

Nick: It makes perfect sense. And I think that's what most enterprise businesses usually don't build products without the input of their customers.

Saeed: Exactly. That's, that's technically the, the way always. I mean, I believe in listening to the customers from day one. If it's quite nice to have a nice idea, but with all respect, I mean, nice ideas are all over the place. It's just important what customer want to have.

Nick: Absolutely.

Tom: I've just, perhaps the last question, but just to come back to those, let's say big international brokers like Marsh or Aon, are you, are you mainly competing against, let's say Marsh and Aon, or are you more competing against other local, less technically orientated companies, let's say, Saudi companies, which don't have this technical developments like your algorithms or other technical stuff. What’s your main competitor you deal with?

Saeed: Well, well, to be honest, I think when it comes to brokerage; it's not only like getting the best insurance policy. Part of the brokerage work is actually the approvals, the service for the customers. There is a lot of, uh, operations, which is going on between like the, the corporates and the insurance company, which the broker does apart from the renewal. So when it comes to Desaisiv and the offerings that we're having, it's actually like complimentary to what brokers are doing, brokers, they have like the, the employees and the staff on the ground to help with the approvals to help follow up with the cases, to help with getting the best service for the different corporates.

When it comes to the analysis and the pricing and all of these different things, as we have observed in the market, it's actually like, uh, it's like an additional, let's say feature some brokers have, some brokers don't have, and surprisingly, as you've mentioned, uh, Tom, I mean, these big, uh, obviously like international brokers, we have few of their customers that actually using our platform yeah. They're looking at it as like what these different brokers are actually offering, it's more like consulting, uh, let's say reports, they're not interactive and they're not tailored to their needs. However, what Desaisiv is offering is a completely something which is tailored to the corporate needs that they can play with, they can change and completely like to the benefit of the customer.

I'm not saying the brokers, they're not really like, they're always like, you know, obviously putting the customers at the top of their priorities, but that's a very diplomatic way of saying it, but at the end of the day, uh, obviously they have interest in, in getting, uh, some commission out of the premium that they're going to get. And obviously if the premium is higher, they're going to get a better commission. However, when it comes to Desaisiv, our model, its subscription. Like it's just straightforward with the corporates and we're just actually trying to make sure that these corporates are getting the best value for money when it comes to their insurance policies.

Tom: Good thank you.

Nick: Makes sense. Thank you so much for your time and insights. Um, I wish you all the best with the fundraising. Sounds like, you know, should be a walk in the park. Um, and yeah all the best in your future endeavours. Thank you so much. And hope to see you at some point face to face on the insurTech circuit. Thank you so much.

Saeed: Thanks a lot, Nick. Thanks a lot, Tom. It was really a pleasure to be hosted by you.

Thank you so much.

Tom: Thank you very much.

Saeed: Bye.