This is a transcription of our interview with Dzeneta Schitton, co-founder at D-Darks. You can watch the original interview here or tune in to the podcast episode here, iTunes, Spotify and other podcast apps be seacrhing "Risk Management Show"

Boris: Welcome to our Interview with Dzeneta Schitton. She is a co-founder at D-Darks, which is a risk tech company introducing data driven risk management approach to real estate and financial risk industries.

The company is based in Austria and is addressing the biggest challenges in the financial risk industry, especially in the current circumstances where remote portfolio and investments supervising is especially important for controlling and predicting risk.


Dzeneta thank you for coming to our interview today.

Dzeneta: Thank you so much for having me.

Boris: My pleasure. Dzeneta, You are one of the co-founders and you started your company recently. Can you tell us a short story about your unique path in the industry? Why was there a need for your solution?

Dzeneta: Yes, of course, in fact, we are both coming from the commercial real estate industry. The other cofounder who is also a product developer is somebody who was in the commercial real estate industry for three decades. And five years ago he started with data analytics education, because we saw that some big things which are coming like social media, like data analytics and we somehow both went into different direction.

He went into predictive analytics and as he has lots of experience in commercial real estate. He saw exactly where are the missing links and where are on one hand these practical inconsistencies, which make the workload very slow and on the other hand where are the opportunities. Data analytics gave us first time a possibility to think about risks strategically, to implement business strategies, which are based on risk, where the risk is not seeing as a cost function only, but also as opportunity functions.

So we saw that in the commercial real estate, there are not no such solutions. Mainly these are business analytics solutions, and the problem is the low frequency data. In commercial real estate, as you know, the data is provided once per month or once per quarter. So we are implementing a statistical modeling in order to derive insights from that data and also we are combining it in some high-frequency data.

We made this a really unique model, which is made from scratch and all algorithms are made from scratch. The solution is a general one, but it's customized for every market and for every client. So when redesigning for each client this solution, you're taking into account the expert opinion of the client, because clients always know the best what are the specifics of their local markets.

And especially in the situations that we have now with coronavirus, they know where to put the focus on. This is exactly what gives competitive advantage to each client, which chooses the same our solution, because everybody will see risk differently, everybody will have a different risk appetite, but of course, for those clients who are investing in real estate and do not have so much experience, we are experienced enough to help them also with consulting, but also in implementing a solution.

Boris: So what was the “aha” moment when you decided to go full time? Because it's very difficult, you both probably have had some good paid job, and then suddenly, you have something new.

Dzeneta: Actually, I went on my own, I'm also in marketing and branding and I got independent even before, but for Christian this was the aha moment where he really had to really leave a good paid positions and jump into something where he believed that there is some kind of thing which would move the needle and, he's somebody who hates bureaucracy, excel lists and he spent all of his life in that.

And recently we made a video, which is on our LinkedIn page and also on YouTube where he talks about his internal motivation to make things simplified. I think that because he is not a data scientist by education, he educated himself for years in this and his internal aha moment was when he got what's possible now.

Because in the old system, you had a best case and the worst case scenarios, you had market reports, you had sensitivity analysis and everything was somehow based on your guts, you made your approximate calculation and it was mostly based on the feeling and experience.

But now when he got this feeling, and experience, it can be implemented together with algorithm and give you a data driven background that you can make based on these strategic decisions. I think this is where he also surprised himself and then he went with a full force into this. And today we are a quite aware of this and we are trying to educate the community also about the possibilities of this technology.

Boris: Do you have a development team, or how did you develop this product?

Dzeneta: The product was totally developed by other co-founder. He is now a mixture of the IT guy and statistician and that's why we took this product was developed now for one year and a half. In between we also talked to the industry. We talked to a network to see what are their challenges what's going on. It was a really a hard work for him but on the other hand, we tried to do a little bit of marketing, a little bit of PR in this really beginning stages to make a good start.

Now we are in the stage where we are already talking to the first pilot clients. We are trying to get one or two or three clients, which would be a midsize real estate investment funds and to implement this and to really test it in this real-life environment and then to go full scale.

Boris: So how does your solution differ from other software providers that might be working in this space. And what are some examples of your customers use cases that you would like improve?

Dzenaeta: When we talk about other solutions, they are somehow two streams in the market. A lot of companies are still working in traditional way in the market. Big players are investing in predictive analytics and in these kinds of models, we don't know exactly in which kind, because of course they don't tell us what they are doing, but with respect to the other solutions, we see that there are many solutions in other industries and also in, for example, residential real estate. In commercial real estate, I think that the problem with this low data frequency is in fact the hurdle that we don't have predictive analytics.

We have some kind of partial solutions, but what differs us from the others is that we make this a unique combination of tools, which is not just relying on historical patterns, because as you know now with the coronavirus, we have this unprecedented situation.

And so that's why many risk models do not work now because they have nothing to learn from. They are too optimistic because the commercial real estate markets were a booming for too long. And we found a way to implement certain other kinds of statistical modeling, which are in fact focusing on the markets, on the risk parameters in the market, which clients see as important. We find connections between those parameters, see what impacts what, for example, with this we are also able to uncover risk clusters in regional sense.

For example, if your portfolio is scattered and you are trying to diversify portfolio in order to control your risk, you are able to find also this, we are then building up a short term prediction, a long term traditional mean midterms prediction.

So I think what's unique is a first of all our, our commercial real estate background, because we really know every little corner of the industry because both of us together, I also, from the legal side we went through, but also the solution was really custom made for real estate, it was custom made to answer to the needs of real estate investment funds, asset management companies.

Also, for example, the other kinds of companies which want to invest in real estate, also banks, which are doing debt financing for a dealer side.

So it's managed as a tool, which can be a cloud solution or API or edge, whatever client wants, it's imagined to be connected with the portfolio. So whatever happens in the market, whatever comes, its outcome is immediately reflected to your liquidity, to all of those important parts of meters in your portfolio.

And our final vision, when you really simplify this vision is that a decision maker has an iPad and on this iPad, they can do simulation, a simply something very easy to do.

Although in the background, it's a quite complex in the final vision, but of course we are in the, in the pilot stage so we will have to work a lot to bring that promising.

Boris: Are you planning to go to your local market and then probably take over the world, what is your marketing plan?

Dzeneta: Our target group is in fact international funds. We can start locally and we are already talking also to Austrian companies, and to German companies, but our ideal client is a company, which has scattered portfolio internationally because with these kinds of portfolios, this solution makes sense.

This solution doesn't make sense if you have a small portfolio, you can handle, this is the excel list. Although we do believe that at the end everybody will need, at least a cloud solution, which will offer predictive view on the market.

Even if you don't implement it with your portfolio, you can have just market solution, or you can have both in combination, or also with consulting.

Boris: Everyone has heard the term, never put all your eggs in one basket.

This is never more than true than in the world of real estate investments. What is the commonly held belief in the real estate investment or a biggest misconception that you strongly disagree with?

Dzeneta: I would say that this defensive relationship to risk. I think in the markets there is not so much information on what the technology is offering us now, I can at least speak for commercial real estate, because this is where we are focused. I saw your interview with Kevin Lester from Validus last time.

And it was an excellent point because you simply have to educate the market that they know what's possible. This is all new to all of us. I think that the one thing that I would like to change is that people can grasp what is possible to do with risk.

Risk in the same time is an opportunity and we don't just sell when we see risk, we also buy, when we come into these situations where we see that they are undervalued assets.

So it's not just to sell what we see is too expensive, but also other way around. So I would like to see more of thinking more strategically about risk and implementing it in the business strategies. Not seeing it, like this isolated island, but also to have it almost like to position yourself like in the chess game, that you always know, two, three steps ahead where you are and where you going.

Boris: So for example, if you take a life of an average risk or a real estate investor manager, what is his worries right now and how your solution can help in solving his problems.

Dzeneta: We see that the main problem people in real estate investment have now is that they simply do not know where the markets are going. Everybody has opinion now. Somebody say that the big insolvency wave is coming and everything is going down. Some other people say, we are a way ahead and there have not been ever better times.

So we see some different, changed market behavior now, especially when we talk about office space, we do not know how people will behave.

We don't know what will companies do. Will companies say, okay, I will pay my workers a bit more, but I keep them at home because I don't have to pay the office space or other way around, let's get all in the office, it was too much.

So a typical real estate investor, who is in commercial real estate, has to think whether to invest further in the office market, will there be demand, or will there vacancy.

And plus over all that, we have the ESG requirements now which also raise certain question.

So we would like to simply implement all risks in these models together with business considerations and with the experience of the experts and our own experience in order to make these models, which are not just risk models, but business models, really usable in practice.

The other thing that I would see as a challenge that we saw it with the traveling ban that we should be ready for the next pandemic, we will always have this fear.

So I think that every decision maker should be able to control his or hers portfolio digitally to connect everything, to have everything in one iPad, not to rely on reporting n Excel lists, on all this bureaucracy, which was created in the last decades, because we didn't have other choice.

And also this would help to use the risk people in more productive ways, because we all worked in banks, we all worked in those companies where you had that time of the year were just lists are going up and down and reports, and you have always something missing in those reports.

I think this will be on one hand to help decision-makers to solve that cost and time issue, but also to solve the really risk issue, which is now rising to new level we didn't have before

Boris: I’m running Global Risk Community, this is an online community for risk managers. I ask this question every guest, how can we contribute to the process of a better understanding of this complex world of risk from your perspective?

Dzeneta: I think education, this is what you do, you do it excellent. I think your podcast is great and it's really from different perspectives. I think that any innovation can happen just in the circumstances of increased level of education, because nobody will innovate with something they don't understand. So I think that our task is to educate and your platform such a great place, which gather so many people and so many great discussions.

As I discovered it, I almost listen to every podcast. And it's interesting to see that when we talk about risk in every industry, it goes in same direction and we all have to follow that. 

And I think that somehow this kind of platforms like yours can be a great mediator between innovation solutions and the industry to help industry understand what is the upside and to grasp on these new solutions.

Because I also think there are lots of foam in the market, lots of unfulfilled promises. There are lots of algorithms that are simplified, like, you just put some data through and something comes out. And I have a feeling like the industry is feeling like they are losing control, especially people who are in this business for 50 years.

They done these things in one way and now you come and you put something, some algorithms, which will tell him what to do.

I think that there is a lot of mistrust because there's a lot of foam in the market. So I think we also should talk about this and there is a way to make our own algorithms transparent, to explain what we are doing, to make a thoughtful process around it, which will then help to put Risk Management on the next level.

Boris: Thank you. Maybe if I forgot something, do you have something to add to our audience?

Dzeneta: No, thank you, these were great questions. So I would maybe invite people to contact us on LinkedIn, risk professionals, also representatives of the industry. We would also be very interested to talk with the representatives of the industry about the challenges they're facing. What is maybe something we don't see and they see, this will help also to help us improve our models. And of course we are inviting everybody interested to come and join us in this discovery process, and we are ready to make the first clients happier about Risk Management then they are now.

Boris: Dzeneta, thank you very much for your interview. I wish you great success with finding new pilot clients, and then expanding to all over the world and hope to speak with you in a few months and see how you are progressing with your company.

Dzeneta: Thank you so much. This was such a great podcast and I do believe that this will happen as we see how the market is reacting to these kind of ideas andI'm very optimistic. I see a lot of progress, so thank you so much.


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