Banks, asset managers weigh trade-offs in third-party tools for machine learning
Although many banks and asset managers still prefer to build models in-house, off-the-shelf products are maturing.
“Build versus buy” is an age-old conundrum in most aspects of financial services enterprise technology. That is no less true for emerging technologies like machine learning than it has been in other, more traditional parts of firms’ tech estates. While off-the-shelf products have improved hugely, and no- and low-code platforms promise to make building models a breeze, many organizations still prefer to build their own algorithms and models.
Andy McMahon, machine-learning engineering lead at
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.
You may share this content using our article tools. Printing this content is for the sole use of the Authorised User (named subscriber), as outlined in our terms and conditions - https://www.infopro-insight.com/terms-conditions/insight-subscriptions/
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. Copying this content is for the sole use of the Authorised User (named subscriber), as outlined in our terms and conditions - https://www.infopro-insight.com/terms-conditions/insight-subscriptions/
If you would like to purchase additional rights please email info@waterstechnology.com
More on Emerging Technologies
S&P debuts Spark Assist genAI copilot, draws up ‘Blueprints’ of combined datasets
S&P’s Kensho subsidiary has rolled out new emerging tech products leveraging AI to explore and combine the vendor’s wealth of datasets to solve common use cases.
Nasdaq reshuffles tech divisions post-Adenza
Adenza is now fully integrated into the exchange operator’s ecosystem, bringing opportunities for new business and a fresh perspective on how fintech fits into its strategy.
Liquidnet sees electronic future for gray bond trading
TP Icap’s gray market bond trading unit has more than doubled transactions in the first quarter of 2024.
Verafin launches genAI copilot for fincrime investigators
Features include document summarization and improved research tools.
Waters Wrap: Open source and storm clouds on the horizon
Regulators and politicians in America and Europe are increasingly concerned about AI—and, by extension, open-source development. Anthony says there are real reasons for concern.
Waters Wavelength Podcast: Broadridge’s Joseph Lo on GPTs
Joseph Lo, head of enterprise platforms at Broadridge, joins the podcast to discuss AI tools.
Man Group CTO eyes ‘significant impact’ for genAI across the fund
Man Group’s Gary Collier discussed the potential merits of and use cases for generative AI across the business at an event in London hosted by Bloomberg.
BNY Mellon deploys Nvidia DGX SuperPOD, identifies hundreds of AI use cases
BNY Mellon says it is the first bank to deploy Nvidia’s AI datacenter infrastructure, as it joins an increasing number of Wall Street firms that are embracing AI technologies.
Most read
- Chris Edmonds takes the reins at ICE Fixed Income and Data Services
- Deutsche Börse democratizes data with Marketplace offering
- Nasdaq reshuffles tech divisions post-Adenza