Outlook 2017: Ari Studnitzer, CME Group01.03.2017 By Rob Daly Editor-at-Large
Ari Studnitzer is managing director of architecture and product management at the CME Group.
How would you complete the sentence: The new year will be known as the year of _____?
What do you see as the next watershed for the industry in 2017?
I believe blockchain is going to continue to have an impact on our industry in 2017 and beyond, and I think we’ll start to see practical applications of blockchain and smart contracts launch in the coming year. For instance, CME Group announced the 2017 launch of a digital gold spot platform with the UK’s Royal Mint, which will enable more efficient trading of gold with blockchain technology.
What do you view as the most important lesson of 2016?
Learning and experimentation are more important than ever. We recently established an Innovation Center at the University of Illinois Research Park that has allowed us to experiment with new technologies, fail-fast, and provide greater flexibility to innovate.
Which hot topics/hype should be retired at the end of 2016?
Resistance to the cloud is definitely a thing of the past. Whether or not the cloud is here to stay is no longer a conversation anymore. Our industry is already moving forward with terms like serverless computing.
What changes do you expect to see in regards to machine learning in 2017?
Artificial intelligence (AI) with machine and deep learning technology has broad implications up and down the value chain for the financial industry, such as enhanced regulatory surveillance for exchanges, better compliance insights for clearing firms, and new strategies for trading firms. The technology has matured to the point where we will likely see applications of AI in the financial industry in 2017. CME Group has already invested in two innovative AI fintech startups, Nervana and SparkCognition, through our independent investment arm, CME Ventures, and we continue to work with additional vendors in the space.
Technology has enhanced capabilities of surveilling larger and more disparate data sets.
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