Golden Copy: Learning to Master the Machines
More machine learning efforts are arriving for financial data management, but are they well guided?
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.
Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content.
To access these options, along with all other subscription benefits, please contact info@waterstechnology.com or view our subscription options here: http://subscriptions.waterstechnology.com/subscribe
You are currently unable to print this content. Please contact info@waterstechnology.com to find out more.
You are currently unable to copy this content. Please contact info@waterstechnology.com to find out more.
Copyright Infopro Digital Limited. All rights reserved.
As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (point 2.4), printing is limited to a single copy.
If you would like to purchase additional rights please email info@waterstechnology.com
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (clause 2.4), an Authorised User may only make one copy of the materials for their own personal use. You must also comply with the restrictions in clause 2.5.
If you would like to purchase additional rights please email info@waterstechnology.com
More on Data Management
Substantive Research reveals new metrics for market data negotiations framework
The research firm will make its industry-derived project available for public consumption next month.
As the ETF market grows, firms must tackle existing data complexities
Finding reliable reference data is becoming a bigger concern for investors as the ETF market continues to balloon. This led to Big xyt to partner with Trackinsight.
Artificial intelligence, like a CDO, needs to learn from its mistakes
The IMD Wrap: The value of good data professionals isn’t how many things they’ve got right, says Max Bowie, but how many things they got wrong and then fixed.
An inside look: How AI powered innovation in the capital markets in 2024
From generative AI and machine learning to more classical forms of AI, banks, asset managers, exchanges, and vendors looked to large language models, co-pilots, and other tools to drive analytics.
As US options market continued its inexorable climb, ‘plumbing’ issues persisted
Capacity concerns have lingered in the options market, but progress was made in 2024.
Data costs rose in 2024, but so did mitigation tools and strategies
Under pressure to rein in data spend at a time when prices and data usage are increasing, data managers are using a combination of established tactics and new tools to battle rising costs.
In 2025, keep reference data weird
The SEC, ESMA, CFTC and other acronyms provided the drama in reference data this year, including in crypto.
Asset manager Saratoga uses AI to accelerate Ridgeline rollout
The tech provider’s AI assistant helps clients summarize research, client interactions, report generation, as well as interact with the Ridgeline platform.