When machine learning goes awry, here's how to do better next time
Executives from JP Morgan, Morgan Stanley, and BNY Mellon discuss the lessons learned through experimenting with machine learning at their firms.
At this year’s North American Financial Information Summit, held on May 17 in Manhattan, machine-learning engineers from JPMorgan, BNY Mellon, and Morgan Stanley discussed where they’ve seen machine-learning (ML) projects go wrong. While some of the lessons learned from failures may seem obvious, inexplicable, opaque ML implementations still plague the industry. Financial professionals want to use ML as a magic bullet, but without a thought-out process and a plan, the end results often
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: https://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 Emerging Technologies
Blackstone partners with Google, BBH and Citi enhance API connectivity, and more
The Waters Cooler: A recap of the major tech and data news from the past week in the capital markets.
Waters Wavelength Podcast Ep. 352: Agentic workflows, AI bootcamps, regulation, and faves from Seoul
This week, Tony and Shen chat about some recent stories.
Old data practices key to navigating new agentic ambitions
Metadata and data quality are not as sexy as autonomous agents, but data executives across the capital markets warn that they are integral to successful agents.
EU AI Act leaves agents in regulatory limbo
A new paper published by AI ethicists draws attention to a hole in the EU AI Act surrounding high-risk agentic systems.
CME to launch compute futures, agentic AI for capital calls, and more
The Waters Cooler: A recap of the major tech and data news from the past week in the capital markets.
APAC’s hidden opportunity is in the hands of wealth managers
Asia-Pacific’s financial firms have lofty growth ambitions that will come with high cost and complexity. To succeed, they’ll need a quality portfolio toolkit and a connected technology architecture, writes BlackRock’s James Verner.
FactSet’s vectorization service aims to improve agent accuracy
FactSet chief AI officer Kate Stepp discusses the importance of having AI-ready data in the agentic era.
DeFi and TradFi firms are borrowing each other’s benefits
The Waters Wrap: As blockchain tech gains a small foothold in market data, Nyela says the thing separating blockchain’s previous craze and its second wind is choice.
