The cloud has come a long way in terms of supporting the financial industry, and over the next few years there will be more and more workloads as well as capabilities moving to the cloud, according to Nikhil Singhvi, Managing Director, Core Trading Technology, Credit Suisse.
Speaking at the Financial Services Summit, part of digital Google Cloud Summit series, Singhvi said: “There is constant pressure on the margins, and I think that, particularly with the advent of new platforms, the efficiency will remain the key, and time to market is going to be key. And both of those can be very well observed with cloud technology.”
For the moderator, Ashish Majmundar, Global Head, Capital Markets, Google Cloud, there are some customers that are early in their journey, and just getting into the cloud, whereas there are others using the cloud in an advanced way.
“Some of the best users are using AI/ML in a very significant way for helping their businesses,” he said.
Lian Wang, SVP, Data Science, HSBC, agreed, saying that: “AI/ML, has great value for us. It opens up new work on analytics by leveraging big data and massive computation powers, so that it can provide an opportunity for us to achieve better stronger model performance that was not ever possible before.”
He stressed, however, that at the same time, AI/ML also brings up new risks: “We need to pay more attention on data products. Any data changes from the upstream or downstream data which are not captured will impact the model performance, and also the model is not going to be very stable.”
When discussing post-trade, Singhvi said the goal of the industry has been to consistently, reduce the settlement time. “The trading volumes are not static, they kind of go up and down all the time. So processing capacity on demand, that’s where I think the cloud comes into the picture.”
He added that the other aspect that can help is in terms of the data sharing. “Reference data is one of the big reasons why there are settlement fake news. Coming up with clean, good quality reference data that can then be shared by everybody in the industry will not only cut down the settlement times but also make the operations more efficient,” he said.
Beyond settlement time and going more into sources of customer experience, “you see more and more companies looking at different sources of data”, according to Majmundar.
“There are other data sources, particularly from a trading standpoint – whether you look at geospatial datasets, credit card data sets or sentiment analysis. When you need to process all this type of data, technologies such as NLP is a big tool, it’s helping us not just in a document analysis, but also in communications,” Singhvi said.
