In this episode of the Connector Podcast, host Koen Vanderhoydonk interviews Andreas Lusser, co‑founder of The Screener, a 24‑year‑old Swiss fintech that provides B2B content for banks, brokers and wealth‑management platforms. The conversation begins with the company’s origin in the spring of 2000, describing how the dot‑com boom gave an easy start but the subsequent crash forged a “storm‑proven” resilience that helped the firm survive later crises such as the 2008 financial crash and COVID‑19. Lusser explains The Screener’s core value proposition: turning the overwhelming flood of financial data into concise, tradable insights for institutional clients. He emphasizes a quality‑first data pipeline – raw data is ingested twice a week, flagged by an error‑detection algorithm, then manually verified before entering the system. This deterministic approach underpins the firm’s AI, which guarantees that identical inputs always produce identical outputs, a requirement for compliance and traceability. The product suite is positioned as a B2B service that equips digital banking platforms and private‑bank advisors with simple, algorithm‑driven checklists. Lusser outlines three fundamental market drivers – analyst forecasts, market sentiment, and fundamental valuation – and how The Screener’s algorithms distill these into clear “with/against” signals for each security. The solution helps advisors answer spontaneous client questions (e.g., sector outlooks) consistently, thereby increasing their share of wallet. The discussion also covers the company’s international growth phase, leveraging its boutique flexibility to adapt to varied market needs, while continuing to evolve its AI capabilities. The episode concludes with contact details (thescreener.com, LinkedIn).
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Welcome to the Connector Podcast, an ongoing conversation connecting fintechs, banks, and regulators worldwide. Join CEO and founder Koen Vanderhoydonk as you learn more about the latest available trends and solutions in the markets.
Welcome to another Connector Podcast, and today we're not going to talk about MacBooks, Windows, but we're going to Talk about the screener, and I have with me Andreas from the screener. So Andreas, can you tell us a little bit what the screener does and who are you?
Hey, thanks so much, Koen, for your interest. Happy to do that. Yes, so I'm Andreas. I'm one of the founders of of the screener. We are a 24-year-old fintech, so we are the Methusalem if you want of the fintechs in the world. We didn't think of the name fintech that was invented much much later. But we are a technology firm in the financial area. And in fact, what we do is we provide content for investors that should help to make tradable insights and cut a little bit through the whole fog of so much data that we have to get to the kind of the so what of all the data which is around.
And you say you have founded the company in the year 2000? That was a very turbulent time, right? A lot of things were happening. The Y2K bug. I think we were coming out of a dot-com boom. So how was that? How— what did it take you to actually start the company?
Well, at the beginning, it wasn't stormy. At the beginning, it was very sunny. Just it didn't stay sunny very long. So when we started in spring 2000, that was still the dot-com boom going on. So initial founding for the company, that was absolutely the easiest thing. you could do, just call yourself dot-com and you have lots of money on the table. That was the easy part of it. It became then much more difficult about half a year later when the dot-com boom turned into a dot-com doom. And in fact, I mean, in hindsight, it was absolutely great in the sense we could start it and then kind of all the noise was cut out by the market because everybody who survived the dot-com doom afterwards, they were really storm-proven. So I think it's You start sunny, but then you get into a big storm and you make it out of it. Then you have seen a couple of storms, more storms were to come. We had the financial crisis, COVID, and lots of other ups and downs. But let's say once you've gone through one or two of the major storms, you know what waves you have to expect and you're not just a sunny sailor.
Nice. I think that's a very important life lesson for all of us listening here today. Now, earlier you said, Andreas, that you have financial analytic tools. You based your company based on data, financial data. But what's the catch? Why is it so quirky? Because data you can basically get everywhere, quote unquote.
Yes, I mean, there is an old saying, you know, we are drowning in data and we are thirsty for information. And in the financial business, that's no different to any other business as well. And I mean, sometimes, I know the question when you do business, the question is differentiation. How do we differentiate ourselves and how do we help our clients differentiate themselves? So first of all, to to understand, very important, is we are B2B. So we don't offer any services for retail investors directly, but we are B2B. So our clients are the online brokers of this world. Our clients are the banks and the financial institutions who want to make their digital channel, their e-banking, their e-brokerage more attractive, their web applications, their mobile applications, what is it. And we have to provide content for them to differentiate themselves. And differentiation. So when it comes, let's say, to this financial data, and, and the problem of it is, um, first of all, there is a lot.
Mm-hmm.
And second, it's not bad, not good quality. And third is you have to make the conclusion out of lots of data yourself. So essentially, um, there are a lot of companies who have technologies and they can access raw data, you know, the numbers, the charts, the financials, the underlyings, the forecasts by the analysts. There are lots of numbers around there which you can find on the usual web information terminals. But the first thing that we do is we don't access this data through an API just automatically like would do any other quantitative company.
Mm-hmm.
But we upload the data only twice a week into our system. And the reason for this is simply quality. That means we have an algorithm that spots potentially erroneous data, and then we have an employee who has to personally check and decide what data to correct before it gets on our machine. So the quality in, quality, you know, garbage in, garbage out, that's unfortunately true even if you apply whatever intelligence, whatever comes. But first you have to get good data. That's the first part. And the second part to differentiate is, in my view, simplicity.
I agree.
Things have to be, have to make sense. You know, if I give you so much reading, It doesn't help you. At the end, you have to make a trade. You buy or you sell this security. You have it and you bought it. You have to decide every day, I keep it or I sell it. I keep it or I sell it. I keep it or I sell it.
It's a very simple rule, isn't it?
That's the decision you have to take. We are not here to write books, to have good readings. We are here to assist our clients, which are, you know, the institutions, so that they can help their end clients to make, within reasonable time, reasonable decisions.
Well, you say that, well, there's 2 things that come to my mind. The first one is you talk about data quality, which you explained. But in that explanation, it sounded almost to me like machine learning. So is this something that you could call it's the new AI trend that you're following? Or was it something you're already doing 20 years ago?
It's a good question. I mean, the good news about this AI now in the last, whatever, 2 years is that finally people understand what we have been doing for 20 years. So that's the, that's the good news.
Excellent.
And if you go behind, let's say, if you go behind the mechanisms, of course, you know, there are different generations and, and, and, and tools and technology becomes more modern. But, um, the basic principle to take data and to make a meaningful outcome is the same. Where we are, uh, let's say, where we stop in terms of artificial intelligence is this generative AI, which means We want to make sure for compliance and many other reasons that the same input must give the same output.
For traceability.
So we have— we use algorithms that make sure that when you have the same input today, you get the same output. And if tomorrow you have the same input, you again get the same output. So there are certain subtleties within, but at the end of the day, it is a form of artificial intelligence that we are applying. Absolutely.
Let's move a bit more to the business side of things. We're talking investments. Investment has different shapes and forms. It can be done by your own execution only. It can be done by a private banker or a wealth manager. So for example, looking at self-directed investors, how would you typically help them in their journey?
Absolutely. I mean, first of all, as I said, we are B2B, so we don't try to, I don't know, get a monthly fee from self-directed investors themselves, but we help the platforms for self-directed investors. And essentially what, what we observe is there are 2 types of self-directed investors. Um, once they are really completely on their own and they have their tools and they trade a lot and they go to the cheapest trader around who offers you cheap and reliable execution it. There are excellent brokers out there who offer you this service. That's not where we come in. These people, they are completely self-directed and they have their own tools and methodology. And the more— the, the bigger part of the self-directed investors, they could be called lonely investors. The problem is, uh, in the old times, even with a small fortune, you had a banker, a broker, an advisor who was at your side. And when you ask them, what do you think about this or that equity, they said, well, why not, you know, that's a good company, and you felt comforted. That's like I say, when you go and buy a pair of jeans, um, you can do that completely alone, or you can change it in the dress room and then nevertheless have your whatever, your friend, your wife, or maybe even the salesperson to tell you, well, it looks good on you. And it is this side where the investor that is lonely because they are just left in the digital room.
Mm-hmm.
You have to give him something to hold their hands and not just conflicting another 500 numbers and things, but to boil it down to the essentials. And we really looked at— Einstein once said that you should tell things as simple as possible, but not simpler either. And when you look at the market, there are essentially 3 drivers that drive the price of any equities. It's the analysts. The analysts are out there. Some like them, some don't. It's not an issue. They're a relevant impact in the market. If they forecast you make a loss, if they forecast to make a profit, that impacts an equity's price. The second force that impacts is the market itself.
Mm-hmm.
If you go against the market, you have a different life than if you go with the market. So the market itself, is the Street buying? Is the Street selling? Is it selling more? And so on. There are aspects to it, technical analysis, behavioral finance. There are a lot of words for it, but at the end, it's the same thing. It's the market itself that is a driver. And the third question is the fundamental price. If you have something which is trading at 10 times what is fundamentally justifiable, you have a different situation than if you have something that looks like a bargain. And what we do is we have lots of algorithms that at the end boil it down to every investment. We give you a checklist that says you're investing with or against the analysts. You're again investing with or against the market, and you follow justifiable fundamental mathematics, yes or no. Now, you can go contrarian if you want, but then you should be at least conscious that you go against the analyst, against the market. And, you know, or you can go with them, but at least it's a very simple boiled-down result out of lots of numbers and a lot of algorithms. That's a bit where we come in, where we really try to simplify it for the end user and to help the platform. So any digital banking platform that they can give this content to their investors and thereby really defend their margin. That's really what the business is about.
And I'm glad that you put it so clearly that it's like information because you're not giving advice, you're informing the client. When you talk about advice, there's advice and discretionary. What are the tools that you then provide, or is there anything that you provide?
There is information, and there is, let's say, there is from the retail investor who wants to have it simple but good. And we have also the professional asset manager who has certain access to platforms that we offer. And a very important part is the advisor in between. So we have the pure asset managers, professional portfolio managers, you have the self-directed investor. And an important part for us is also the client advisor. Um, put it— put yourself in the role of the manager of a private wealth institution, of your whatever private banker organization. You have 100 or 1,000 or whatever different, uh, front client-facing individuals in your organization.
Mhm.
Now the question is, if you want to increase your share of wallet, you have to make your client advisors look smarter than the ones of the competition. That's the obvious part. The question is, how do you do that? And The moment of truth is always when the client challenges you with something.
True.
If a client says, look, I just won in the lottery, I want to invest my money, typically any wealth management institution has their recommendation list and they're well prepared for this question. But if somebody just challenges you and said, how do you see at the moment the semiconductors? How do you see at the moment Intel versus NVIDIA? How do you see at the moment this or that? That's a spontaneous question which you cannot plan because there are thousands, even millions of different types of questions that can appear. And thanks to our machine-based algorithm, we can cover almost anything and make sure that your client-facing teams have the better answers for stupid questions. And that essentially results that the next spontaneous question from your client comes to you and not to the other institution where the other part of the money is.
Yeah.
So that's for us very important to assist the client-facing people because they, they have a terribly difficult, uh, situation. The client expects you that they have a knowledge gap, they have a knowledge lead over you, otherwise you wouldn't— why should you call them and pay them? On the other hand, information in the internet is just so completely available to everybody. So it's quite a challenge for institutions to give your client-facing professionals a real edge over your And that's essentially where we have certain services that we offer.
Yeah. And I think I would like also the fact, if I would be a private banker, that you said that the same input gives the same output. So knowing that everyone in your team sings the same song, it's a very powerful thing also as a bank, because then you know that there's no compliance and conflicting things within the private bank force, I can imagine. We're almost at the end of our conversation. Time flies when we're talking innovation and talking about our personal passions. How do you see the future of the screener and what do you see in the market evolving?
We have been around for many years. We have been a bit traditionally— hear my accent, I'm a Swiss person. We are a Swiss-based company. We have subsidiaries in different countries. But clearly, we have been extremely successful and active around our our kind of home place and the surrounding places. And we feel, uh, first of all, growth is, uh, is international. We see a lot of demand now for our products also outside, where we are just simply— maybe we have, you know, we, we have whatever, maybe we have— they should take more ales earlier on. But the data is around for all the markets almost worldwide. So it's a question just of, of, of marketing presence internationally. So that's Very clearly, we are in an international growth phase that we started and the feedback we get is really fantastic.
Super exciting.
It's extremely exciting, extremely exciting to see the needs which are somehow similar but yet a little bit different in the different countries. And as we are a boutique, we are able to really listen to the different markets and to come up with solutions that suit. And that is really, that is something. On the general product development side, of course, Of course, this AI, but that's a buzzword nobody wants to hear anymore. So I'm sure there are millions of podcasts on this. I will not go into more details. Just skip on this one. Yes, of course, that's something as we have been in this field for more than 20 years, of course, we follow every generation that comes and there's a lot to come. But I save you and your listeners the details of yet another speech on artificial intelligence.
All right, great. Great. I think what you can give us though, Andreas, is to let us know how people can contact you and The Screener.
I mean, we have a homepage, of course, thescreener.com.
That's an easy one.
That's an ad, if you allow me to make this.
Of course.
You'll find me on LinkedIn. We work with the connector that's fantastic. So you can find us easily on the usual, either LinkedIn, Andreas Lusser, or on our homepage, thescreener.com.
Well, brilliant. Well, Andreas, thank you very much for having you. Thank you also to the audience, and stay tuned for more news out of this wonderful fintech world.
Koen, thanks so much.
You're welcome.
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