Less than a decade ago if you talked to most anyone on Wall Street, they would have said that there’s no chance that they would use a public cloud provider to store data. Now, most firms have at least some data stored in Amazon’s, Google’s or Microsoft’s cloud environment.
That is all to say that change happens fast. While much of the talk in 2017 has been about blockchain, the technology that is truly transforming the markets is artificial intelligence (AI). While the discipline is old, its uses are becoming more applicable to capital markets firms. As a result, we decided to add this category to the BST Awards line-up for 2017 as various AI techniques are revolutionizing the industry. And the inaugural winner of the best buy-side AI platform is Indus Valley Partners (IVP).
The main reason why the judges went with IVP is because it is incorporating several forms of AI into its offerings, from machine learning to natural-language processing (NLP) and deep learning. Take, for example, its Sentiment Analysis engine, which incorporates a big-data platform and machine learning to provide analytics on portfolio performance. The engine connects to over 100 market data sources and leverages genetic algorithms and NLP techniques to heuristically search through millions of news feeds, Twitter messages and other alternative datasets and then suggests quantitative sentiment value to the instrument being analyzed. “We started with reconciliations—it was a logical use-case—but we then turned our attention to the front office,” says Gurvinder Singh, founder and CEO of IVP.
To that point, the New York-based vendor first addressed the middle office by building an AI engine into its reconciliation platform, IVP Recon. The solution learns to identify breaks, flags non-normal activity and automatically suggests solutions. Beyond a rules-based engine, it recognizes, reacts and resolves issues in patterns within data in real time. According to the firm, IVP Robo Recon engine has the ability to automatically manage the entire reconciliation process in less than five minutes, and that’s just by looking at the raw data. It turns the reconciliations process into a self-learning environment by applying break-resolution solutions and automatically suggesting matches.
“Over the last two to five years, machine learning has gone main stream. As a tech firm, we were always watching the space and trying to figure out how we could leverage it in the context of our industry,” Singh says. “There was a lot of debate about RPA and AI, but we didn’t really view RPA as being that transformational. When we think about AI and machine learning, we see a variety of use-cases from the front office, all the way to the back and middle offices.”
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