A.I. Gets Its Close-Up

Artificial intelligence is on the rise as a potential solution for data issues in the financial industry, with some qualifications


Artificial intelligence has frequently been a hot idea for Hollywood to draw on for sci-fi movies. Artificial intelligence also periodically gets talked about in the data management realm, and those conversations are multiplying.

Last year, I wrote a column about artificial intelligence in both these realms— comparing the movie "Her," and its depiction of a virtual personal assistant who has inhuman command of a large number of clients, with a seemingly chaotic world of big data that business and finance concerns are trying to figure out how to manage.

A.I. is back in the movies recently with the hit "Ex Machina," in which a neophyte is introduced to artificial intelligence as embodied by a female robot, and "Chappie," in which a droid learns to think and feel on its own. And the next "Terminator" movie will be out in just a few weeks. The interest in A.I. as evident in these pieces of pop culture is also high among start-ups. At last week's Exponential Finance conference in New York, Neil Jacobstein, co-chair of artificial intelligence and robotics at Singularity University, the organizer of the conference, rattled off more than 10 start-ups or new innovative companies, all doing something interesting with A.I. in one form or another.

Not all of these may have been applicable to the reference data management realm within the financial industry, but a few are. AltX uses machine learning applied to data to make suggestions to portfolio managers. Dataminr applies machine learning to social media to produce information that is useful for the financial industry in real-time—this one is more akin to real-time market data than reference data. Lastly, Verafin applies fraud detection techniques to address know-your-customer and anti-money laundering issues, particularly for customer risk management.

WorkFusion, an artificial intelligence and machine learning provider covered previously in Inside Reference Data, has notably been applying A.I. to corporate actions data, which itself is a rapidly expanding subset of financial reference data.

Jacobstein explained the need to build massive artificial intelligences, like the companies he cited are doing, "because of the accelerating wave of human knowledge," he said. Jacobstein expects the most interesting capabilities and results to come from artificial intelligence and human knowledge being combined, citing former chess champion Gary Kasparov, who has been studying artificial intelligence and concluded that A.I. and people working together can be more powerful at solving problems than A.I. by itself. This suggests the possibility that artificial intelligence could alter the balance of power between big, established companies and start-ups.

First, however, artificial intelligence will have to become more accessible to ever smaller companies, and the question remains whether some of the start-ups Jacobstein cites can make their offerings available at a cost within reach for smaller firms and funds. If those potential users are enabled with A.I. to manage and derive meaning from increasingly larger volumes of data, then there can be the A.I. "revolution" that Singularity University and its conference envisions.

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