Finastra Uses Machine Learning for Fat-Finger Detection
The new piece of technology aims to tackle issue of erroneous trades at the source.
Named FusionCapital Detect, the algorithm uses machine learning to track clearly erroneous trades before they can go through the entire post-trade process—and halt them before they become a headache for treasury departments and possibly end the careers of clumsy traders.
Finastra, which was formed in mid-2017 through the merger of Misys and D+H, is preparing to sign its first deal for the product, which began through its Fusion Reactor innovation program, Nadeem Syed, the company’s chief
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
Data vendors are vagueposting about MCP prices
Exchanges and data providers are charging for AI access to their data. Nobody will say how much.
Goldman’s shift on AI highlights the gap between prototype and production
James Harrison expects more institutions will take AI tools away from their traders and bankers—and for good reasons.
Americans voted against datacenters. Datacenters may still win.
The Waters Wrap: The US midterms could spell trouble for datacenters but only if Dems put the screws to Big Tech, too, Reb writes.
AI for data quality control catches on at institutions
Data execs at banks and asset managers say they are working on projects using AI to improve data-quality workflows.
A Q&A with BofA’s Global Markets CIO on AI and problem-solving
Following the bank’s Global Markets Innovation Summit, Duncan McInnes says AI is helping his teams “reimagine” established processes and ask where technology can make them simpler and faster.
Compute is leaving land. Recoverability isn’t following.
The limits of land may push datacenters to the ocean or even outer space, writes Vishal Gupta. But these options introduce new challenges.
Who’s counting the agent army?
The Waters Wrap: Agents are growing faster than the controls to manage them. Wei-Shen says that as firms struggle to measure their returns on investment, it may come down to who foots the bill.
Asia’s T+1 journey splinters as Hong Kong delays and Malaysia mulls
APAC markets are setting their own timelines for shorter settlement, while Deutsche Bank proposes an “earlier certainty” mechanism to ease the cross-border strain.