Lydia Linna: Next Gate Tech is a fintech that offers managers an overview of assets and risks. What exactly does that mean, and what makes your company’s software special?
Davide Martucci: What we do is split into two main axes. The first is what we call data management. We’re plugged in with different fund service providers--administrators, depositories, brokers--and we capture data on a daily basis. As you can imagine, all those different service providers have a unique way of reporting the data. So the first thing we provide is a normalisation of the data. We ingest, harmonise and enrich the data so that our clients can use it.
The second part is that we perform analytics based on this clean and enriched data. It can then be used to calculate the value of that fund, or help asset managers and service providers see if there are mistakes in the calculation of the net asset value. Once you have the harmonised and enriched data, you can do pretty much whatever you want--client reporting, regulatory reporting, oversight, risk management.
We do that through our software as a service model.
Talking with experts in the field of tech and innovation for their 2023 forecasts, we’ve heard a lot about how cyberattacks will be on the rise. How can companies ensure that their data is secure?
I think there’s a huge evolution in the mindset of the industry, as well as for regulators, in terms of using cloud-based solutions for security as well.
Then you might imagine that the question is: at Next Gate Tech, you have 30 people in Luxembourg. How can you protect against a massive cyberattack?
The answer is that we use solutions that are developed from big tech providers such as Google. We use their Google Cloud Platform solution to store the data, to perform all the calculations, including all the machine learning calculations, but as well to have all the layers of information security that are needed.
I think that opened the door for a company like us, which has 30 people. You have all the different controls and processes in place, but you can also rely on the technologies and expertise of big companies like Google, Amazon and Microsoft in terms of information security.
You’ll be discussing the future of fund data during a session at the Kneip conference on Wednesday. Would you be able to give us a sneak peek of what you and the other panellists will be talking about?
We will have very skilled and experienced panellists, and it will be a very interesting discussion in terms of the data already being used in the industry today, addressing the value of the data that is extracted [and] for which kind of applications data will be useful for the fund industry.
Then we’ll project a bit more into the future: what will be the challenges? What will be the different opportunities? And what will be the world of tomorrow for the fund industry, regarding data?
We’ll also be talking about data strategy in big organisations, the place of skills of humans and what kind of skills will be needed.
What do you think is the biggest threat or challenge that Luxembourg will face next year? What steps should Luxembourg authorities and companies take to make sure that the grand duchy remains competitive?
I don’t think it’s a particular threat for Luxembourg; I think it’s common for the entire industry. Talent is a key challenge. Luxembourg has extremely talented people for accounting, but they need to attract more people in the data science and data engineering roles.
I’m a big advocate of partnership with education. I think that Luxembourg is already doing a lot with that--with Luxinnovation, as well as with the University of Luxembourg. But I think that even more could be done in terms of public-private partnership and education and research. This could help to attract the kind of skills and talents that we need in the data sciences. It’s about partnership between academia, public entities and private companies.
Are there any technological innovations in the fund industry that you’re expecting in the coming year?
I think the fund industry is more and more adopting the use of time series in their approach. It’s not a new technology, it’s not a new methodology; it’s something that has been out there for a long time. It’s [widely] used in investments in the front side but not particularly in the operation of the funds.
When you start to create a series of data points--from yesterday, two days ago, three days ago--this is something that is key to being able to use statistical models, machine learning models and eventually AI. If you want to be able to really digitise the fund industry, you need to switch into a more time series-oriented mindset.
And I think that this is something that in the next year, or next couple of years, the industry will really be doing. I can be way more powerful in terms of harmonisation if I have the history of this data. I can map this data more easily and spot anomalies because I know what is normal. I think the power of time, in terms of data, is something that will be key for using new methodologies such as statistical machine learning and AI in the operation of the fund industry.