Outlook 2017: Ann Neidenbach, Convergex

This entry is part 21 in the series Outlook 2017


Ann Neidenbach is the Chief Information Officer of Convergex.

What do you view as the most important lesson of 2016?

This past year was another evolutionary year for technology in the trading world. The technology environment is changing so quickly that “ideas” evolve into “industry standards” in a matter of months. Whether it’s blockchain technology – which started off as merely industry chatter yet quickly transformed into a solution that the industry is looking to leverage in order to streamline the clearing and settlement process – or migrating infrastructure into IAAS offerings in the cloud. It also saw an explosion of data challenging the industry in turning all of the data into useful information.

Ann Neidenbach, ConvergEx

Ann Neidenbach,

Convergex is meeting these market challenges (and many others) by engaging in a blockchain consortium where we are looking at implementing T0-settlement solutions in U.S. markets and having our Millennium ATS be a T0 matching destination. We also made the commitment to migrate to the cloud and have already started our Cloud transformation initiative, with a target to be fully migrated by the end of 2017. Finally, we spent most of 2016 building a business intelligence dashboard leveraging data visualization in order to help us derive meaningful information from all the data we have. In short, the lesson here for the financial industry is to continuously evaluate the innovations in fintech to see how your business can leverage and differentiate itself with the latest technology.

What changes do you expect to see in regards to financial technology, such as machine learning, in 2017?

I anticipate that machine learning will really gain traction in 2017. Firms will be looking into how they can leverage self-learning and adaptive technology, live-streaming data in real-time, automating intelligence from that data, and optimizing performance. Here at Convergex, we are considering upgrading our signaling technology to further enhance our algorithms and to take advantage of the machine learning technology to build correlation signals and predictive models. This technology will enable us to filter through massive amounts of data with the goal of finding more opportunities for our customers by leveraging cognitive trading solutions.

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