The Data Revolution: The Disruption Moves Upstream .

Artificial Intelligence, machine learning and text mining – What is the newest disruptor to the financial world?

The Data Revolution: The Disruption Moves Upstream

By Oliver Albers / Head of Sales, Global Information Services

Data Disruption blog image MI January 2016

I, like many of you, have been following the conversations in our industry around disruptive innovation. I even had the opportunity to present in a panel on the topic last fall. It raised the question: what will be the next huge change going to be in financial services?

In my view, a significant disruption in the capital markets—especially on the equities side—has already taken place. For years, we have been focused on finding analytics in less than a microsecond and being faster than the next guy. And, we succeeded. But the speed game is all but over in the financial services. Now the disruption has migrated upstream from the capital markets to the utmost areas of the supply chain, where the business decisions for a particular industry are being made.

Man versus Machine: who will make the investment decision? We see a number of new innovations on the horizon. Artificial Intelligence (AI) technologies such as machine learning, text mining, and natural language processing can bring major disruption to the investment process. Just look at the number of traders already out of work, simply because of smart order automation and automated financial advisors. And the “robo advisors” operate on a set of algorithms, rather than AI alone. Just imagine if they were able to teach themselves to make well-informed decisions based on data sets. Machine learning is still in its infancy; thus, there is likely much more disruption ahead.

More data than ever. Truly, we are in the midst of a data revolution. There is an unprecedented amount of data available; in fact, out of all industries, the financial services industry has the highest percentage of big data usage.[i] Given the relatively low storage costs available, as well as the growing phenomenon of disruptive technologies, the financial industry is increasingly able to mine, ingest and analyze more data than ever. However, the industry players still need to determine how to process all of this data—from a structured vs. unstructured perspective, from a volume perspective, etc. How will it be stored? What new and value-added analytics can be produced?

Shift in spending. We believe that in 2016, we will continue to see a shift in how firms allocate budget, increasing their investment in new hires and new technology. Many firms have been and will continue to spend more money on hardware and software; on data gurus and chief data officers to manage the new data sets; and on building out new analytical capabilities. Those firms that haven’t yet embraced new technologies will likely need to get on board quickly, either by investing in technology enhancements or by outsourcing their analytics and other data management needs.

While there are firms that are already fully automated, we believe that the automation phenomenon is going to become more and more prevalent and sophisticated. Smart Beta indexes already contribute to the spread of automation, as does the growth of passive investing.

Analytics, analytics, analytics. The word is spoken constantly, but what are the true opportunities there? Analytics—the discovery and communications of meaningful patterns in data—will be further advanced by the technologies being developed. With increasing frequency, new and different techniques arise for combining core data sets with third party data—resulting in fresh analytical data that will offer firms new alpha opportunities or to allow them to mitigate risk.

In conclusion, there are myriad opportunities for value-added analytics in the fintech space, especially upstream. At Nasdaq, we cannot wait to experience the next disruption that the data revolution will bring.

Follow our initiatives in the fintech space at www.business.nasdaq.com/fintech

[i] At 22%. Source: Oracle (blog).

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