Golden Copy: Learning to Master the Machines

More machine learning efforts are arriving for financial data management, but are they well guided?

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Does the industry's willingness to deploy artificial intelligence for data operations issues match its will to direct and check on how that AI is being used, and evaluate how useful it is?

This past week saw two new machine learning efforts for financial data management – Bloomberg's Liquidity Assessment Tool, just launched, and StockViews, a crowdsourcing investment platform that reaped new funding for applying machine learning and artificial intelligence to enhance its crowdsourced research on companies.

We have also seen other machine learning initiatives for financial data in recent weeks and months. In late February, Velocimetrics, a performance measurement and analytics provider, announced that it had added machine learning techniques to its market data quality solution.

Last year, WorkFusion executive Adam Devine shared how the company was applying artificial intelligence to the automation of repetitive data processing tasks. And IIROC, Canada's major self-regulatory organization, has completed a machine learning project to segment market participants.

Also last year, in this column, I identified AltX, Dataminr and Verafin as companies that are making use of machine learning in different ways to yield greater insights from data, whether for portfolio managers or for compliance purposes.

These add up to quite a few machine learning ventures, and could be just the tip of the iceberg within the financial industry. The question that must be asked is whether the hands guiding any or all of these efforts are using machine learning processes effectively to gain more useful insights from data in order to produce intelligence that is indeed actionable.

Often, the rationale for using machine learning is indeed automation of data processing, as WorkFusion does. Automating data processing produces efficiency, but doing so with artificial intelligence or machine learning is the key factor in raising data quality, or at least avoiding the decline in data quality that would inevitably occur in automation without an intelligence factor to reduce errors.

Since last year, judging by the emergence of these recent new ventures, confidence in machine learning and artificial intelligence seems to be continuing its rise. Yet even the efforts begun less recently must still build a track record of effectiveness and value for their users.

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