In December 2018, Thomas Hejlsberg joined SimCorp as our new CTO. Thomas brings experience from 15 years as CTO / Chief Architect at Microsoft, where he has been part of the transformation of Dynamics 365 Business Central (formerly known as Dynamics NAV) from an on-prem 2-tier solution to a full software-as-a-service (SaaS) cloud offering. Sharing SimCorp’s cloud vision, our CTO explains in this article how we are positioning to service the business models of the future investment management industry.
When a company is moving to the cloud today, the typical driver is to alleviate the cost of infrastructure and instead buy the software as a service (known as SaaS). The argument is that we should buy and use software just like we buy and consume electricity or a streaming service. Nobody would consider having their own tv-studio just to watch tv.
It not only makes sense, moving to a subscription-based SaaS solution also offers many advantages, as opposed to running an on-premise software solution. Among the many benefits are: Automated upgrades, continuous delivery, dynamic scaling, etc. Without a doubt, all these services represent a huge step forward as they allow firms to concentrate on their core business.
SimCorp is already offering to deliver its core system, SimCorp Dimension, as a service, and an increasing number of our clients are utilizing this option. If you have not made the move to the cloud, I can only recommend starting your transition today, rather than tomorrow. If you are not yet convinced, keep on reading.
This is only the beginning
The true promise of the cloud is unleashed when we start combining services. You might say that we can make 1+1 become 3. But it doesn’t stop there. Machine learning (ML) and artificial intelligence (AI) will be able to provide us with something additional: “Insights” – and this is where there is no limit to how much 1+1 can add up to.
To understand insights, however, we first need to get a general understanding of ML. Let’s start with what is greatest about it: There is no magic involved and it is easily understood. Just like we can watch a movie without knowing exactly what it takes to make one, you can say the same thing about ML.
The core capability of ML is the ability to predict something based on existing (historic) data.
We do it ourselves all the time. If a train has been on time 99 times in a row, we predict that it will be on time the 100th time as well. If our teenage son has overslept five out of the last ten days, we would not expect him to be on time the next ten days, suddenly making a perfect attendance. Rather, we would predict that he will not make it.
Try to translate this reasoning into a common business scenario. Can we trust a given customer with a line of credit? Well, by help of our historic data, we can find out whether other customers within the same line of business, having been in business approximately the same amount of time, having a comparable revenue, etc., made it. Based on this data, we can predict the risk of our new customer defaulting payments.
The ML functionality simply finds patterns in the data matching the facts of historic data and applies the pattern to the new data to form a prediction. Thomas Hejlsberg, CTO, Senior Vice President, SimCorp