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.
![weight](/sites/default/files/styles/landscape_750_463/public/2021-04/kurt-liebhaeuser-FPZqKwT_S_M-unsplash.jpg.webp?itok=6d60gsmn)
“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
AI could cut time for money laundering checks by 99%
Leading crypto exchange rolling out large language model for enhanced due diligence checks.
Standard Chartered keeps faith with quantum experimentation
The bank is aiming to future-proof itself with the ability to adopt new technology at an early stage.
Waters Wrap: CME, Google and the pursuit of ultra-low-latency trading
CME Group and Google have announced Aurora, Illinois, as the location for the exchange’s new co-location facility. Anthony explains why this is more than just the next phase of the two companies’ originally announced project.
This Week: Genesis/Interop.io; S&P Global; Finos/OS-Climate and more
A summary of the latest financial technology news.
GenAI: US Fed reveals its five use cases
Internal sandbox used to assess viability and risks; coding and content generation on the agenda.
Natixis refines in-house interoperability model
The French asset manager has refined its canonical data model over the last decade, as the interoperability movement continues to evolve.
UK asset manager: AI in macro trading ‘very overblown’; useful for nowcasting
The managing partner of Fulcrum Asset Management said that the firm has been developing nowcasting tools that even central banks have consulted on.
The coming AI revolution in QIS
The first machine learning-based equity indexes launched in 2019. They are finally gaining traction with investors.